Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning. - Kun Wang 058; WaiChing Sun (CoRR, 2018)

A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation. - Kun Wang 058; WaiChing Sun; Qiang Du (CoRR, 2019)

A phase field model for cohesive fracture in micropolar continua. - Hyoung Suk Suh; WaiChing Sun; Devin O'Connor (CoRR, 2020)

ILS-MPM: an implicit level-set-based material point method for frictional particulate contact mechanics of deformable particles. - Chuanqi Liu; WaiChing Sun (CoRR, 2020)

Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity. - Nikolaos Vlassis; Ran Ma; WaiChing Sun (CoRR, 2020)

A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks. - Kun Wang 058; WaiChing Sun; Qiang Du (CoRR, 2020)

An immersed phase field fracture model for fluid-infiltrating porous media with evolving Beavers-Joseph-Saffman condition. - Hyoung Suk Suh; WaiChing Sun (CoRR, 2020)

Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening. - Nikolaos N. Vlassis; WaiChing Sun (CoRR, 2020)

An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data. - Bahador Bahmani; WaiChing Sun (CoRR, 2020)

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph. - Chen Cai; Nikolaos Vlassis; Lucas Magee; Ran Ma; Zeyu Xiong; Bahador Bahmani; Teng-Fong Wong; Yusu Wang 001; WaiChing Sun (CoRR, 2021)

Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation. - Xiao Sun; Bahador Bahmani; Nikolaos N. Vlassis; WaiChing Sun; Yanxun Xu (CoRR, 2021)

Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings. - Bahador Bahmani; WaiChing Sun (CoRR, 2021)

MD-inferred neural network monoclinic finite-strain hyperelasticity models for β-HMX: Sobolev training and validation against physical constraints. - Nikolaos N. Vlassis; Puhan Zhao; Ran Ma; Tommy Sewell; WaiChing Sun (CoRR, 2021)

Manifold embedding data-driven mechanics. - Bahador Bahmani; WaiChing Sun (CoRR, 2021)

A new finite element level set reinitialization method based on the shifted boundary method. - Tianju Xue; WaiChing Sun; Sigrid Adriaenssens; Yujie Wei; Chuanqi Liu (J. Comput. Phys., 2021)

Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity. - Nikolaos N. Vlassis; WaiChing Sun (CoRR, 2022)

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter. - Rubén Villarreal; Nikolaos N. Vlassis; Nhon N. Phan; Tommie A. Catanach; Reese E. Jones; Nathaniel A. Trask; Sharlotte L. B. Kramer; WaiChing Sun (CoRR, 2022)

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties. - Nikolaos N. Vlassis; WaiChing Sun (CoRR, 2023)

Synthesizing realistic sand assemblies with denoising diffusion in latent space. - Nikolaos N. Vlassis; WaiChing Sun; Khalid A. Alshibli; Richard A. Regueiro (CoRR, 2023)

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions. - Bahador Bahmani; Hyoung Suk Suh; WaiChing Sun (CoRR, 2023)

Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria. - Karl A. Kalina; Philipp Gebhart; Jörg Brummund; Lennart Linden; WaiChing Sun; Markus Kästner (CoRR, 2023)

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials. - Bahador Bahmani; WaiChing Sun (CoRR, 2023)

Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks. - Jaehong Chung; Rasool Ahmad; WaiChing Sun; Wei Cai; Tapan Mukerji (CoRR, 2023)

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables. - Max Rosenkranz; Karl A. Kalina; Jörg Brummund; WaiChing Sun; Markus Kästner (CoRR, 2024)

A review on data-driven constitutive laws for solids. - Jan Niklas Fuhg; Govinda Anantha Padmanabha; Nikolaos Bouklas; Bahador Bahmani; WaiChing Sun; Nikolaos N. Vlassis; Moritz Flaschel; Pietro Carrara; Laura De Lorenzis (CoRR, 2024)

Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions. - Karl A. Kalina; Jörg Brummund; WaiChing Sun; Markus Kästner (CoRR, 2024)

Physics-Informed Diffusion Models. - Jan-Hendrik Bastek; WaiChing Sun; Dennis M. Kochmann (ICLR, 2025)

Towards scientific machine learning for granular material simulations - challenges and opportunities. - Marc Fransen; Andreas Fürst; Deepak Tunuguntla; Daniel N. Wilke; Benedikt Alkin; Daniel Barreto; Johannes Brandstetter; Miguel Angel Cabrera; Xinyan Fan; Mengwu Guo; Bram Kieskamp; Krishna Kumar; et al. (CoRR, 2025)