A material frame approach for evaluating continuum variables in atomistic simulations. - Jonathan A. Zimmerman; Reese E. Jones; Jeremy A. Templeton (J. Comput. Phys., 2010)

A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations. - Maher Salloum; Khachik Sargsyan; Reese E. Jones; Bert J. Debusschere; Habib N. Najm; Helgi Adalsteinsson (Multiscale Model. Simul., 2012)

Quantifying Sampling Noise and Parametric Uncertainty in Atomistic-to-Continuum Simulations Using Surrogate Models. - Maher Salloum; Khachik Sargsyan; Reese E. Jones; Habib N. Najm; Bert J. Debusschere (Multiscale Model. Simul., 2015)

Machine learning strategies for systems with invariance properties. - Julia Ling; Reese E. Jones; Jeremy A. Templeton (J. Comput. Phys., 2016)

Tensor Basis Gaussian Process Models of Hyperelastic Materials. - Ari Frankel; Reese E. Jones; Laura P. Swiler (CoRR, 2019)

Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model. - Ari Frankel; Kousuke Tachida; Reese E. Jones (Mach. Learn. Sci. Technol., 2020)

Mesh-based graph convolutional neural network models of processes with complex initial states. - Ari Frankel; Cosmin Safta; Coleman Alleman; Reese E. Jones (CoRR, 2021)

A heteroencoder architecture for prediction of failure locations in porous metals using variational inference. - Wyatt Bridgman; Xiaoxuan Zhang; Gregory H. Teichert; Mohammad Khalil; Krishna C. Garikipati; Reese E. Jones (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)

Deep learning and multi-level featurization of graph representations of microstructural data. - Reese E. Jones; Cosmin Safta; Ari Frankel (CoRR, 2022)

Modular machine learning-based elastoplasticity: generalization in the context of limited data. - Jan Niklas Fuhg; Craig M. Hamel; Kyle Johnson; Reese E. Jones; Nikolaos Bouklas (CoRR, 2022)

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations. - Wyatt Bridgman; Reese E. Jones; Mohammad Khalil (CoRR, 2023)

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. - Jan Niklas Fuhg; Reese E. Jones; Nikolaos Bouklas (CoRR, 2023)

Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty. - Saibal De; Reese E. Jones; Hemanth Kolla (CoRR, 2023)

Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy. - Wyatt Bridgman; Uma Balakrishnan; Reese E. Jones; Jiefu Chen; Xuqing Wu 001; Cosmin Safta; Yueqin Huang; Mohammad Khalil (CoRR, 2023)

Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes. - Jeremiah Hauth; Cosmin Safta; Xun Huan; Ravi G. Patel; Reese E. Jones (CoRR, 2024)

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response. - Ravi G. Patel; Cosmin Safta; Reese E. Jones (CoRR, 2024)

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. - Govinda Anantha Padmanabha; Jan Niklas Fuhg; Cosmin Safta; Reese E. Jones; Nikolaos Bouklas (CoRR, 2024)

Inverse design of anisotropic microstructures using physics-augmented neural networks. - Asghar A. Jadoon; Karl A. Kalina; Manuel K. Rausch; Reese E. Jones; Jan Niklas Fuhg (CoRR, 2024)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks. - Govinda Anantha Padmanabha; Cosmin Safta; Nikolaos Bouklas; Reese E. Jones (CoRR, 2024)

Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger-Rivlin-Ericksen. - Jan Niklas Fuhg; Nikolaos Bouklas; Reese E. Jones (J. Comput. Inf. Sci. Eng., 2024)

A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity. - Ryan Yan; D. Thomas Seidl; Reese E. Jones; Panayiotis Papadopoulos (CoRR, 2025)

Input Specific Neural Networks. - Asghar A. Jadoon; D. Thomas Seidl; Reese E. Jones; Jan Niklas Fuhg (CoRR, 2025)

Uncertainty quantification of neural network models of evolving processes via Langevin sampling. - Cosmin Safta; Reese E. Jones; Ravi G. Patel; Raelynn Wonnacott; Dan S. Bolintineanu; Craig M. Hamel; Sharlotte L. B. Kramer (CoRR, 2025)

Differentiable neural network representation of multi-well, locally-convex potentials. - Reese E. Jones; Adrian Buganza Tepole; Jan Niklas Fuhg (CoRR, 2025)

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials. - Steven Yang; Michal Levin; Govinda Anantha Padmanabha; Miriam Borshevsky; Ohad Cohen; D. Thomas Seidl; Reese E. Jones; Nikolaos Bouklas; Noy Cohen (CoRR, 2025)