Reese E. Jones

30 papers Journal 30

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Differentiable neural network representation of multi-well, locally-convex potentials. J. Comput. Phys. Reese E. Jones, Adrian Buganza Tepole, Jan Niklas Fuhg
2025	J	`jnl`	A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity. CoRR Ryan Yan, D. Thomas Seidl, Reese E. Jones, Panayiotis Papadopoulos
2025	J	`jnl`	Differentiable neural network representation of multi-well, locally-convex potentials. CoRR Reese E. Jones, Adrian Buganza Tepole, Jan Niklas Fuhg
2025	J	`jnl`	GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks. CoRR Tian Yu Yen, Reese E. Jones, Ravi G. Patel
2025	J	`jnl`	Input Specific Neural Networks. CoRR Asghar A. Jadoon, D. Thomas Seidl, Reese E. Jones, Jan Niklas Fuhg
2025	J	`jnl`	Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials. CoRR Steven Yang, Michal Levin, Govinda Anantha Padmanabha, Miriam Borshevsky, Ohad Cohen, D. Thomas Seidl, Reese E. Jones, Nikolaos Bouklas, Noy Cohen
2025	J	`jnl`	Thermodynamically Consistent Hybrid and Permutation-Invariant Neural Yield Functions for Anisotropic Plasticity. CoRR Asghar A. Jadoon, Ravi G. Patel, Brian Neal Granzow, Reese E. Jones, D. Thomas Seidl, Jan Niklas Fuhg
2025	J	`jnl`	Uncertainty quantification of neural network models of evolving processes via Langevin sampling. CoRR Cosmin Safta, Reese E. Jones, Ravi G. Patel, Raelynn Wonnacott, Dan S. Bolintineanu, Craig M. Hamel, Sharlotte L. B. Kramer
2024	J	`jnl`	Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks. CoRR Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas, Reese E. Jones
2024	J	`jnl`	Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response. CoRR Ravi G. Patel, Cosmin Safta, Reese E. Jones
2024	J	`jnl`	Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. CoRR Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta, Reese E. Jones, Nikolaos Bouklas
2024	J	`jnl`	Inverse design of anisotropic microstructures using physics-augmented neural networks. CoRR Asghar A. Jadoon, Karl A. Kalina, Manuel K. Rausch, Reese E. Jones, Jan Niklas Fuhg
2024	J	`jnl`	Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger-Rivlin-Ericksen. J. Comput. Inf. Sci. Eng. Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
2024	J	`jnl`	Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes. CoRR Jeremiah Hauth, Cosmin Safta, Xun Huan, Ravi G. Patel, Reese E. Jones
2023	J	`jnl`	Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty. CoRR Saibal De, Reese E. Jones, Hemanth Kolla
2023	J	`jnl`	Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy. CoRR Wyatt Bridgman, Uma Balakrishnan, Reese E. Jones, Jiefu Chen, Xuqing Wu, Cosmin Safta, Yueqin Huang, Mohammad Khalil
2023	J	`jnl`	Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. CoRR Jan Niklas Fuhg, Reese E. Jones, Nikolaos Bouklas
2023	J	`jnl`	Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations. CoRR Wyatt Bridgman, Reese E. Jones, Mohammad Khalil
2023	J	`jnl`	Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. CoRR Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
2022	J	`jnl`	A heteroencoder architecture for prediction of failure locations in porous metals using variational inference. CoRR Wyatt Bridgman, Xiaoxuan Zhang, Gregory H. Teichert, Mohammad Khalil, Krishna C. Garikipati, Reese E. Jones
2022	J	`jnl`	Deep learning and multi-level featurization of graph representations of microstructural data. CoRR Reese E. Jones, Cosmin Safta, Ari Frankel
2022	J	`jnl`	Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter. CoRR Rubén Villarreal, Nikolaos N. Vlassis, Nhon N. Phan, Tommie A. Catanach, Reese E. Jones, Nathaniel A. Trask, Sharlotte L. B. Kramer, WaiChing Sun
2022	J	`jnl`	Modular machine learning-based elastoplasticity: generalization in the context of limited data. CoRR Jan Niklas Fuhg, Craig M. Hamel, Kyle Johnson, Reese E. Jones, Nikolaos Bouklas
2021	J	`jnl`	Mesh-based graph convolutional neural network models of processes with complex initial states. CoRR Ari Frankel, Cosmin Safta, Coleman Alleman, Reese E. Jones
2020	J	`jnl`	Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model. Mach. Learn. Sci. Technol. Ari Frankel, Kousuke Tachida, Reese E. Jones
2019	J	`jnl`	Tensor Basis Gaussian Process Models of Hyperelastic Materials. CoRR Ari Frankel, Reese E. Jones, Laura P. Swiler
2016	J	`jnl`	Machine learning strategies for systems with invariance properties. J. Comput. Phys. Julia Ling, Reese E. Jones, Jeremy A. Templeton
2015	J	`jnl`	Quantifying Sampling Noise and Parametric Uncertainty in Atomistic-to-Continuum Simulations Using Surrogate Models. Multiscale Model. Simul. Maher Salloum, Khachik Sargsyan, Reese E. Jones, Habib N. Najm, Bert J. Debusschere
2012	J	`jnl`	A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations. Multiscale Model. Simul. Maher Salloum, Khachik Sargsyan, Reese E. Jones, Bert J. Debusschere, Habib N. Najm, Helgi Adalsteinsson
2010	J	`jnl`	A material frame approach for evaluating continuum variables in atomistic simulations. J. Comput. Phys. Jonathan A. Zimmerman, Reese E. Jones, Jeremy A. Templeton