We are a part of the multi-university team who has been awarded a 2 million grant for leveraging interpretable  augmented intelligence for multiscale discovery. 

Award Abstract #1940203

Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery

NSF Org:Office of Advanced Cyberinfrastructure (OAC)

ABSTRACT

The ability to model, predict, and improve the mechanical performance of engineering materials such as polymers, composites, and alloys can have a significant impact on manufacturing, with important economic and societal benefits. As advanced computational algorithms and data science approaches become available, they can be harnessed to disrupt the current approaches to materials modeling, and allow for the design and discovery of new high-strength, high-performance materials for manufacturing. Bringing together multidisciplinary teams of researchers can maximize the impact of these new tools and techniques. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) award supports the conceptualization of an Institute to develop novel data science methods, address fundamental scientific questions of Materials Engineering and Manufacturing, and build such multidisciplinary teams. The project will apply novel data science methods to advance the analysis of large sets of structural data of composite materials and alloys from the atomic scale to correlate with and predict mechanical properties. The methods are based on machine learning techniques and uncertainty quantification, and will help uncover underlying structural features in the materials that determine the properties and performance. The methods and results will help accelerate the development of ultra-high strength and lightweight carbon-based composites for aerospace applications, and multi-element superalloys for more durable engine parts, by navigating in the large possible design space and providing faster predictions than experiments and traditional simulation methods. The project will also lead to new methods and computational algorithms that will become publicly available. The investigators will train graduate and undergraduate students from various disciplines with a focus on engaging women and minorities in STEM fields, develop short courses that integrate novel Materials Science and Engineering applications and Data Science methods, and foster vertical integration of interdisciplinary research from undergraduate students to senior scientists.

This project aims at building an effective and interpretable learning framework for materials data across scales to solve a major challenge in current data-driven materials design. The combined Materials Science and Data Science approaches will synergistically contribute to the development and use of interpretable and physics-informed data science methodologies to gain new understanding of mechanical properties of polymer composites and alloys, with the potential to be expanded into different property sets and different systems. The PIs will utilize available data efficiently through combination with physical rules and prior knowledge, to develop an interpretable augmented intelligent system to learn principles behind the association of input structures with material properties with uncertainty quantification. The interconnected tasks involve the (1) collection and curation of large amounts of computational and experimental data for polymer/carbon nanotube composites and alloys from open data sources and targeted calculations and experiments, (2) the development of geometric and topological methods incorporating physical principles to generate a better, more sensitive low-dimensional representation of the multidimensional data and characterize the parameter space related to mechanical properties, (3) the development of a Bayesian deep reinforcement learning framework to generate interpretable knowledge graphs that depict the relational knowledge among physical quantities with uncertainty quantification, and (4) the prediction of mechanical properties to reveal design principles to improve materials performance, evaluate and validate the methods, and develop software for dissemination.

This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. 

More information can be found in the official announcement from NSF [URL].  

A configurational force for adaptive re-meshing of gradient-enhanced poromechanics problems with history-dependent variables​

SeonHong Na, Eric C Bryant, Waiching Sun
​
We introduce a mesh-adaption framework that employs a multi-physical configurational force and Lie algebra to capture multi-physical responses of fluid-infiltrating geological materials while maintaining the efficiency of the computational models. To resolve sharp changes of both displacement and pore pressure, we introduce an energy-estimate-free re-meshing criterion by extending the configurational force theory to consider the energy dissipation due to the fluid diffusion and the gradient-dependent plastic flow. To establish new equilibria after re-meshing, the local tensorial history-dependent variables at the integration points are first decomposed into spectral forms. Then, the principal value and direction are projected onto a smooth field interpolated by the basis function of the finite element space via the Lie-algebra mapping. Our numerical results indicate that this Lie algebra operator, in general, leads to a new trial state closer to the equilibrium than the ones obtained from the tensor component mapping approach. A new configurational force for dissipative fluid-infiltrating porous materials that exhibit gradient-dependent plastic flow is introduced such that the re-meshing may accommodate the need to resolve the sharp pressure gradient as well as the strain localization. The predicted responses are found to be not influenced by the mesh size due to the micromorphic regularization, while the adaptive meshing enables us to capture the width of deformation bands without the necessity of employing fine mesh everywhere in the domain. [PDF]

2019-07-29
#1971999 - A Cooperative Two-player Game for Data-driven Discovery of Elasto-plasticity Knowledge Represented in Directed Graph
02:00 - 02:20
Minisymposium#203 Data-driven Modeling Using Uncertainty Quantification, Machine Learning and Optimization
Authors Kun Wang ,WaiChing Sun * ,Qiang Du
LocationR oom # Lone Star Ballroom F - LVL 3
​We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.

2019-07-31
#1971644 - Data-driven Validation of Bishop’s Effective Stress Principle through Deep Reinforcement Learning
02:20 - 02:40
Minisymposium#105 Computational Geomechanics, in honor of Prof. Mary F. Wheeler
Authors Yousef Heider * ,Kun Wang ,Hyoung Suk Suh ,WaiChing Sun
Location Room # Lone Star Ballroom C - LVL 3

In analogy to Terzaghi’s effective stress principle in fully-saturated porous media, Bishop’s effective stress principle presents a partition of the total stress in unsaturated porous media between suction and effective stress contributions. Although widely applied in Geomechanics, the validity and generality of Bishop’s principle across saturation regimes still a matter of debate, especially for path-dependent materials that exhibit hysteresis retention behaviors. The proposed meta-modeling automated learning approach makes use of data-based constitutive modeling, generated via reinforcement learning, to evaluate the validity and generality of Bishop’s effective stress principle for various types of blind predictions involving material failures, such as strain localization and brittle fracture. Synthetic micro-structural-based data of unsaturated granular materials (such as coordination number, fabric tensor, porosity, saturation, suction, and intrinsic permeability) are generated using the discrete element method DEM together with the pore-network approach, applied to a representative volume element (RVE). Data set generated from sub-scale simulations are used to automatically create, train, calibrate and validate plausible alternative stress partition theories represented by directed graphs until an optimal knowledge graph is formed. The blind prediction performance of the data-driven discovery will then be compared with predictions based on different variations of the effective stress principle for unsaturated porous media.

2019-07-31
#1971799 - A Micromorphic-regularized Cam-clay-type Model for Capturing Size-dependent Anisotropy in Geological materials
05:30 - 05:50
Minisymposium#105 Computational Geomechanics, in honor of Prof. Mary F. Wheeler
Authors Eric Bryant* ,WaiChing Sun
LocationRoom # Lone Star Ballroom C - LVL 3
We introduce a micromorphic-regularized anisotropic modified Cam-clay model which captures the size-dependent anisotropic elastoplastic responses for clay, mudstone, shales and sedimentary rock. To capture the distinctive anisotropic effect induced by the micro-structures of clay particle aggregate, clusters, peds, micro-fabric and mineral contact, we use a mapping the links the anisotropic stress state to a fictitious stress space to introduce anisotropy to the modified Cam-clay model at the material point scale. Meanwhile, the meso-scale aniostropy is captured via an anisotropic micromorphic regularization model such that the gradient-enhanced plastic flow may exhibit anisotropic responses via a diffusivity tensor. This diffusivity tensor enables the micromorphic regularized model to exhibit plastic flow non-co-axial to the stress gradient of the yield function without introducing non-associate flow rules and hence provide additional degree of freedom for modelers to capture the size-dependent ansiotropy of geological materials that exhibit different anisotropic responses across different length scales. Numerical examples are used to examine the volumetric locking and numerical stability issues that may occur at critical state where isochoric plastic flow dominates the deformation mode. In particular, we present evidence that the micromorphic regularization could also be a potential remedy to overcome the volumetric locking and the spurious checkerboard modes. The influence of the size-dependent anisotropy on the formation and propagation of shear band in the anisotropic material is demonstrated. In the future, we will explore coupling to a phase field fracture model, in order to predict the wide spectrum of brittle and ductile anisotropic responses.

Abstracts: 
We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.  Preprint available at ResearchGate [URL]. 

Abstract:

We introduce a regularized anisotropic modified Cam-clay (MCC) model which captures the size-dependent anisotropic elastoplastic responses for clay, mudstone, shales, and sedimentary rock. By homogenizing the multiscale anisotropic effects induced by clay particle aggregate, clusters, peds, micro-fabric, and mineral contact across length scales, we introduce two distinctive anisotropic mechanisms for the MCC model at the material point and mesoscale levels. We first employ a mapping that links the anisotropic stress state to a fictitious isotropic principal stress-space to introduce anisotropy at the material point scale. Then, the mesoscale anisotropy is introduced via an anisotropic regularization mechanism. This anisotropic regularization mechanism is triggered by introducing gradient-dependence of the internal variables through a penalty method such that the resultant gradient-enhanced plastic flow may exhibit anisotropic responses non-coaxial to the stress gradient of the yield function. The influence of the size-dependent anisotropy on the formation of the shear band and the macroscopic responses of the effective media are analyzed in 2D and 3D numerical examples. [PDF]

Abstract: 
We introduce a mathematical framework designed to enable a simple image-to-simulation workflow for solids of complex geometries in the geometrically nonlinear regime. While the material point method is used to circumvent  the mesh distortion issues commonly exhibited in Lagrangian meshes,
a shifted domain technique originated from [Main and Scovazzi, 2018a,b] is used to represent the boundary conditions implicitly via a level set or signed distance function. Consequently, this method completely bypasses the need to generate high-quality conformal mesh to represent complex geometries and therefore allows modelers to select the space of the interpolation function without the constraints due to the geometrical need. This important simplification enables us to simulate deformation of complex geometries inferred from voxel images. Verification examples on deformable body subjected to finite rotation have shown that the new shifted domain material point method is able to generate frame-indifferent results. Meanwhile, simulations using microCT images of a Hostun sand have demonstrated that this method is able to reproduce the quasi-brittle damage mechanisms of single grain without the excessively concentrated nodes commonly displayed in conformal meshes that represent 3D objects with local fine details. [PDF]

WaiChing "Steve" Sun, an assistant professor in the Department of Civil Engineering and Engineering Mechanics, is part of a team who recently won a highly competitive Department of Defense (DoD) MURI (Multidisciplinary University Research Initiative) grant to develop computational/data-driven/machine-learning-enhanced mathematical models for energetic materials with an integrated experimental and modeling efforts across university. The team is led by University of Missouri-Columbia, and includes researchers from University of Iowa, UIUC, Rensselaer Polytechnic Institute, Purdue University, and Columbia.  The five-year $7.5 million AFOSR (Air Force Office of Scientific Research) grant was awarded for the DoD’s “MURI Topic #24: Microstructurally-Aware Continuum Models for Energetic Materials;” the project is titled “Integrating Multiscale Modeling and Experiments to Develop a Meso-Informed Predictive Capability for Explosives Safety and Performance” (See press release from Department of Defense here). Since its inception in 1985, the tri-service (ARO, ONR, AFOSR) MURI program has been supporting teams whose members have diverse sets of expertise as well as creative and different approaches to tackling problems. It’s a program that remains a cornerstone of the DOD’s legacy of scientific impact. 

Sun's work focuses on the development of theoretical and computational models and the corresponding computer algorithms for porous media, with applications in geomechanics and computational mechanics, and mechanics for civil infrastructure. This is the first DoD MURI grant obtained by Sun and the seventh Department of Defense grant Sun's research group has obtained since 2014. His work is supported by multiple federal funding agencies, including two highly competitive Young Investigator Program Awards from Army Research Office (ARO) and Air Force Office of Scientific Research (AFOSR), an 800K grant from Department of Energy Nuclear University Program (DOE NEUP) on nuclear waste disposal, and recently an NSF CAREER award from the mechanics of materials and structures program of CMMI division in NSF. 

1. 2019 workshop to celebrate Prof. J.S. Chen's 60th birthday

11:00am – 11:15am March 11th  Alameda Room, ​The Pleasanton Marriot Hotel, Pleasanton, CA

An Adaptive Meta-Modeling Game for Automated Generations of Elasto-Plasticity Models with Self-Guided Discovery

Abstract:  We introduce a meta-modeling game based on concepts from multi-graph theory to find the optimal way to generate data and write models for blind predictions of a physical process We consider an idealized situation in which the modeling process of history-dependent process can be represented by a sequence of decision making where modelers make choices to formulate a sequence of actions to generate models.  While previous work on data-driven modeling often focus on completely replacing hand-crafted theory with a data-driven paradigm, our goal is to seek the best option that represents the hierarchy of material responses, i.e. the knowledge represented by a directed graph. As such, we introduce a new concept where all the modeling options can be recast as a directed multi-graph and each instant/configuration of the model can be understood as a path that links the source of the directed graph (e.g. strain history) to the sink (e.g. stress). This treatment enables us to further conceptualize the hybrid modeling process as a selection of the optimal choices in a decision tree search via deep reinforcement learning.  In the case where availability of data is limited, the meta-modeling algorithm also explores the weakness links in the constitutive laws and explore the optimal set of experiments to yield the best forward predictions under a limited budget. ​

2. Stanford Department Seminar 

4:30pm to 5:30pm March 12th, Shriram 104, Stanford University

Phase field damage-plasticity framework for fluid-infiltrating materials with size-dependent anisotropy

Abstract: Rock salt and clay are the prime candidates considered for nuclear waste geological disposal. In both materials, the microstructural attributes, such as the crystalline slip system, micro-fabric and mineral contacts, may lead to distinctive anisotropic responses at different length scales. In this talk, we present a unified mathematical framework designed to capture the size-dependent anisotropy of these path-dependent hydro-mechanical responses of geological materials used for nuclear waste disposal. In the brittle regime, we introduce a phase field fracture model in which the difference in critical energy release rates for different kinematics modes is considered. A search algorithm is used to determine the kinematic mode and propagation direction of the crack. In the ductile regime, we introduce a non-coaxial anisotropic regularization of the anisotropic modified Cam-Clay (MCC) model proposed by Semnani et al. 2016 to generate size-dependent plastic flow that could be non-coaxial to the stress gradient of the yield function. The influence of the size-dependent anisotropy on the formation of the shear band and the macroscopic responses of the effective media are analyzed in 2D and 3D numerical examples. The coupling between the damage and plastic responses of anisotropic materials, the transition from the brittle to ductile regimes and corresponding the poromechanical configurational force mesh adaption strategy required to capture the sharp gradients of pore pressure and phase field are also highlighted.  

The Mineral, Metals & Materials Society has published the report on "Verification & Validation of Computational Models Associated with the Mechanics of Materials" in which Professor Sun was served as the chair of the Planning Team. The report can be downloaded at http://tinyurl.com/y3slf8nf. Another related workshop titled "Advanced Computation and Data in Materials and Manufacturing: Core Knowledge Gaps and Opportunities" in which Professor Sun also participated is also available at http://tinyurl.com/y2bhjg79. We are thankful to the participants for contributing to these invited workshops, the members of the organization committee for their time and efforts, and the Mechanics of Materials and Structure Program of National Science Foundation for providing the important support, and the colleagues from TMS for making these workshop a great success. 

Prof. Sun has won the NSF CAREER award for his project, “Deep-reinforcement-learning-enhanced computational failure mechanics across multiple scales.”

Abstract ID: 417225 
Abstract Title: A Modified Phase Field Model for Mixed-Mode Crack Propagation with Consistent Kinematic Modes 
Final Paper Number: H21P-0991 
Presentation Type: Poster 
Session Date and Time: Tuesday, 11 December 2018; 08:00 - 12:20 
Session Number and Title: H21P: Risk Assessment of Fluid-Driven Fracturing in Heterogeneous Geologic Media: Numerical, Experimental, and Field Observations Posters 
Location: Convention Center; Hall A-C (Poster Hall)

In this work, we introduce a single-player game in which we attempt to use the formation of directed graph to represent the thought process / decision process of writing a cohesive zone model. In this work, the AI uses deep reinforcement learning to form knowledge of mechanics represented by directed graph, this knowledge is then used to generate constitutive law. Unlike previous supervised learning method, the automatically discovered/generated/implemented cohesive zone model is robust, accuracy and interpretable by human. Full details can be from the article [URL]. The second one will be coming soon. 

My PhD student Kun Wang has successfully defended his PhD qualification exam. His PhD thesis proposal "From multi-scale modeling to meta-modeling of poromechanics problems" is examined by the committee consisted of Santiago and Roberta Calatrava Family Professor George Deodatis (CEEM), and Fu Foundation Professor Qiang Du, and myself. In the proposed meta-modeling approach, Kun proposes a new method in which one uses a directed multi-graph to represent mechanics knowledge and then uses AI to form a directed graph that leads to a constitutive law. Furthermore, the AI also learns to improve its skill to write constitutive laws through practicing. Unlike previous ML which often leads to blackbox predictions and demands large amount of data, the resultant model is interpretable by human, can be trained with the same amount of data as the hand-crafted counterpart and yet much faster than sub-scale simulations. 

We thank all the committee members for their insightful questions, comments and time.

Kun Wang joined the research group in 9/2014, first as master student, then advanced to PhD in 1/2015.  His thesis focuses on the multiscale modeling and meta-modeling of porous media across multiple length and temporal scales. He has published 7 papers (including 4 CMAME papers) of which he served as the first author to 6 of them.  His work is supported by ARO, AFOSR, DOE, NSF and Columbia Engineering Seed Grant. His achievement and contribution to our research group are exemplified in the published papers, which are listed below. His recent work on data-driven multiscale modeling of porous media has been awarded him a travel grant to present at the Santa Fe Meshless workshop and selected as one of the finalists in the WCCM poster competitions (along with two other group members SeonHong Na and Eric Bryant). The slides of the qualification exam can be found at the bottom of this post. 

Congratulations for advancing to the final chapter of your PhD study, Kun! 

​Published Work:

• K. Wang, W.C. Sun, A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain, Computer Methods in Applied Mechanics and Engineering,  doi:10.1016/j.cma.2016.02.020, 2016. [DRAFT]
• K. Wang, W.C. Sun, Anisotropy of a tensorial Bishop's coefficient for wetted granular materials, Journal of Engineering Mechanics, doi:10.1061/(ASCE)EM.1943-7889.0001005, 2015. [DRAFT] [Bibtex]
• K. Wang, W.C. Sun, S. Salager, S. Na, G. Khaddour, Identifying material parameters for a micro-polar plasticity model via X-ray micro-CT images: lessons learned from the curve-fitting exercises, accepted, International Journal of Multiscale Computational Engineering, 2016. [DRAFT]
• K. Wang, W.C. Sun, A unified variational eigen-erosion framework for interacting fractures and compaction bands in brittle porous media, doi:10.1016/j.cma.2017.01.017, Computer Methods in Applied Mechanics and Engineering, 2017. [DRAFT]
• K. Wang, W.C. Sun,   A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning​, Computer Methods in Applied Mechanics and Engineering, 334(1):337-380, doi:10.1016/j.cma.2018.01.036, ​2018. 
• R. Gupta, S. Salager, K. Wang, W.C. Sun, Open-source support toward validating and falsifying discrete mechanics models using synthetic granular materials Part I: Experimental tests with particles manufactured by a 3D printer, Acta Geotechnica, doi:10.1007/s11440-018-0703-0, 2018. 
• K. Wang, W.C. Sun, updated Lagrangian LBM-DEM-FEM coupling model for dual-permeability porous media with embedded discontinuities, Computer Methods in Applied Mechanics and Engineering, 10.1016/j.cma.2018.09.034, 2018. 
• K. Wang, W.C. Sun, Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning, under review.

An updated Lagrangian LBM-DEM-FEM coupling model for  dual-permeability fissured porous media with embedded discontinuities 

Kun Wang & WaiChing Sun

Many engineering applications and geological processes involve embedded discontinuities in porous media across multiple length scales (e.g. rock joints, grain boundaries, deformation bands and faults). Understanding the multiscale path-dependent hydro-mechanical responses of these interfaces across length scales is of ultimate importance for applications such as CO2 sequestration, hydraulic fracture and earthquake rupture dynamics. While there exist mathematical frameworks such as extended finite element and assumed strain to replicate the kinematics of the interfaces, modeling the cyclic hydro-mechanical constitutive responses of the interfaces remains a difficult task. This paper presents a semi-data-driven multiscale approach that obtains both the traction-separation law and the aperture-porosity-permeability relation from micro-mechanical simulations performed on representative elementary volumes in the finite deformation range. To speed up the multiscale simulations, the incremental constitutive updates of the mechanical responses are obtained from discrete element simulations at the representative elementary volume whereas the hydraulic responses are generated from a neural network trained with data from lattice Boltzmann simulations. These responses are then linked to a macroscopic dual-permeability model. This approach allows one to bypass the need of deriving multi-physical phenomenological laws for complex loading paths. More importantly, it enables the capturing of the evolving anisotropy of the permeabilities of the macro- and micro-pores. A set of numerical experiments are used to demonstrate the robustness of the proposed model. [DRAFT]

PhD student Kun Wang and postdoc research scientist Dr. Chuanqi Liu have received travel grant to present their work at the upcoming Meshfree and Particle Methods: Application and Theory, to be held in Santa Fe, NM, September 10-12, 2018. Chuanqi will present his previous work conducted at Tsinghua University, while Kun will discuss his work on metal-modeling of complex materials in the poster session. The support will cover the registration and travel for both team members. We thank the organizers for providing the support to us. 

It is my great pleasure to announce that my PhD student SeonHong Na has accepted a tenure-track position as assistant professor in the department of civil engineering at McMaster University in Canada. He will officially join McMaster in the spring of 2019. SeonHong joined our research group as PhD student in Fall 2014 after obtained his B.S. (2008) and M.S. (2010) in civil engineering from Seoul National University, Korea. Prior to joining Columbia, he worked as a civil engineer for two years (Kunhwa, Korea) and spent another two years in Coastal Development & Ocean Energy Division in Korea Institute of Ocean Science and Technology (KIOST) as a research scientist. SeonHong's research is currently funded by Army Research Office (frozen soil) and the DOE NEUP (crystalline rock) as well as the Fulbright Fellowship and has just recently graduated in July 2018. He is the 2nd PhD graduated from our group and the 3rd research group members who secured tenure-track position, following Yang Liu (Northeastern) and Jinhyun Choo (University of Hong Kong) since 2014. His published journal articles during the last 3+ years at Columbia are listed below. 

Congratulations again, SeonHong! ​

• S. Na, W.C. Sun, Wave propagation and strain localization in a fully saturated softening porous medium under the non-isothermal conditions, International Journal for Numerical and Analytical Methods in Geomechanics, doi:10.1002/nag.2505, 2016. [PDF][Bibtex]
• K. Wang, W.C. Sun, S. Salager, S. Na, G. Khaddour, Identifying material parameters for a micro-polar plasticity model via X-ray micro-CT images: lessons learned from the curve-fitting exercises, International Journal of Multiscale Computational Engineering, 14(4):389-413,  doi:10.1615/IntJMultCompEng.2016016841, 2016. [PDF][Bibtex]
• S. Na, W.C. Sun, Computational thermo-hydro-mechanics for multiphase freezing and thawing porous media in the finite deformation range, Computer Methods in Applied Mechanics and Engineering, 2017. [DRAFT][Bibtex]​
• S. Na, W.C. Sun, H. Yoon, M. Ingraham, Effects of elastic heterogeneity on the fracture pattern and macroscopic effective toughness of Mancos Shale in Brazilian Tests, Journal of Geophysical Research:Solid Earth, accepted, ​2017.
• S.H. Jang, S. Na, Y-L Park, Magnetically Assisted bilayer composites for soft bending actuators, materials, 10(6), 646, doi:10.3390/ma10060646, 2017. [PDF] (unaffiliated research conducted with the Dr. Sung-Hwan Jang). 
• S. Na, W.C. Sun, Computational thermomechanics of crystalline rock. Part I: a combined multi-phase-field/crystal plasticity approach for single crystal simulations, Computer Methods in Applied Mechanics and Engineering, accepted, 2018.
• S. Na, E.C. Bryant, W.C. Sun, Capturing size effects of anisotropic fluid-infiltrating materials via a higher-order Cam-clay-type model with property-preserving adaptive meshing, in preparation., in preparation. 
• S. Na, W.C. Sun,  phase field modeling of fracture opening, interfacial frictional closure and capillary-driven flow in porous media, in preparation. 

 

Our team member and PhD student SeonHong Na has sucessfully defended his PhD thesis which entitled "Multiscale thermo-hydro-mechanical-chemical (THMC) coupling effects for fluid-infiltrating dual-porosity crystalline rock: theory, implementation, and validation" is examined by the committee consisted of Professor  Hoe Ling (CEEM), Professor Ioannis Kougioumtzoglou (CEEM), Professor Greard Ateshian (ME), Dr. Moo Lee & Dr. Hongkyu Yoon (Sandia National Laboratories). We thank all the committee members for their insightful questions, comments and time.

​

SeonHong Na joined the research group in 9/2014.  His thesis focuses on the computational mechanics of geological media in extreme environments. His work is supported by ARO, DOE, Columbia and Fulbright Fellowship. During his study at Columbia, SeonHong has won numerous awards, including two Teaching Assistance Excellence Awards, the Mindlin Scholarship, Dongju Lee Memorial Award, and the 2nd place from the ASCE EMI Modeling Inelasticity & Multiscale Behavior poster competition, among others.  His achievement and contribution to our research group are exemplified in the thesis and the published papers, which are listed below. 

​Published Work:

• S. Na, W.C. Sun, Wave propagation and strain localization in a fully saturated softening porous medium under the non-isothermal conditions, International Journal for Numerical and Analytical Methods in Geomechanics, doi:10.1002/nag.2505, 2016. [PDF][Bibtex]
• K. Wang, W.C. Sun, S. Salager, S. Na, G. Khaddour, Identifying material parameters for a micro-polar plasticity model via X-ray micro-CT images: lessons learned from the curve-fitting exercises, International Journal of Multiscale Computational Engineering, 14(4):389-413,  doi:10.1615/IntJMultCompEng.2016016841, 2016. [PDF][Bibtex]
• S. Na, W.C. Sun, Computational thermo-hydro-mechanics for multiphase freezing and thawing porous media in the finite deformation range, Computer Methods in Applied Mechanics and Engineering, 2017. [DRAFT][Bibtex]​
• S. Na, W.C. Sun, H. Yoon, M. Ingraham, Effects of elastic heterogeneity on the fracture pattern and macroscopic effective toughness of Mancos Shale in Brazilian Tests, Journal of Geophysical Research:Solid Earth, accepted, ​2017.
• S.H. Jang, S. Na, Y-L Park, Magnetically Assisted bilayer composites for soft bending actuators, materials, 10(6), 646, doi:10.3390/ma10060646, 2017. [PDF] (unaffiliated research conducted with the Dr. Sung-Hwan Jang). 
• S. Na, W.C. Sun, Computational thermomechanics of crystalline rock. Part I: a combined multi-phase-field/crystal plasticity approach for single crystal simulations, submitted to Computer Methods in Applied Mechanics and Engineering, 338:657-691, 2018. [PDF]
• S. Na, E.C. Bryant, W.C. Sun, Capturing size effects of anisotropic fluid-infiltrating materials via a higher-order Cam-clay-type model with property-preserving adaptive meshing, in preparation., in preparation. 
• S. Na, W.C. Sun,  phase field modeling of fracture opening, interfacial frictional closure and capillary-driven flow in porous media, in preparation. 

Congratulations for this well-deserved achievement, Dr. Na!