Reese E. Jones

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 10

Top Topics

Learning Strategies

Variational Inference

Top Venues

J. Comput. Phys.

Publications

Ravi G. Patel, Cosmin Safta, Reese E. Jones
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response. CoRR (2024)
Jeremiah Hauth, Cosmin Safta, Xun Huan, Ravi G. Patel, Reese E. Jones
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes. CoRR (2024)
Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta, Reese E. Jones, Nikolaos Bouklas
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. CoRR (2024)
Wyatt Bridgman, Reese E. Jones, Mohammad Khalil
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations. CoRR (2023)
Jan Niklas Fuhg, Reese E. Jones, Nikolaos Bouklas
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. CoRR (2023)
Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. CoRR (2023)
Saibal De, Reese E. Jones, Hemanth Kolla
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty. CoRR (2023)
Wyatt Bridgman, Uma Balakrishnan, Reese E. Jones, Jiefu Chen, Xuqing Wu, Cosmin Safta, Yueqin Huang, Mohammad Khalil
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy. CoRR (2023)
Rubén Villarreal, Nikolaos N. Vlassis, Nhon N. Phan, Tommie A. Catanach, Reese E. Jones, Nathaniel A. Trask, Sharlotte L. B. Kramer, WaiChing Sun
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter. CoRR (2022)
Julia Ling, Reese E. Jones, Jeremy A. Templeton
Machine learning strategies for systems with invariance properties. J. Comput. Phys. 318 (2016)
Jonathan A. Zimmerman, Reese E. Jones, Jeremy A. Templeton
A material frame approach for evaluating continuum variables in atomistic simulations. J. Comput. Phys. 229 (6) (2010)