Reese E. Jones
Orcid: 0000-0002-2332-6279
According to our database1,
Reese E. Jones
authored at least 22 papers
between 2010 and 2025.
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Bibliography
2025
A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity.
CoRR, January, 2025
2024
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks.
CoRR, 2024
Inverse design of anisotropic microstructures using physics-augmented neural networks.
CoRR, 2024
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.
CoRR, 2024
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response.
CoRR, 2024
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes.
CoRR, 2024
2023
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy.
CoRR, 2023
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty.
CoRR, 2023
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.
CoRR, 2023
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen.
CoRR, 2023
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations.
CoRR, 2023
2022
Modular machine learning-based elastoplasticity: generalization in the context of limited data.
CoRR, 2022
Deep learning and multi-level featurization of graph representations of microstructural data.
CoRR, 2022
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter.
CoRR, 2022
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference.
CoRR, 2022
2021
Mesh-based graph convolutional neural network models of processes with complex initial states.
CoRR, 2021
2020
Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model.
Mach. Learn. Sci. Technol., 2020
2019
2016
J. Comput. Phys., 2016
2015
Quantifying Sampling Noise and Parametric Uncertainty in Atomistic-to-Continuum Simulations Using Surrogate Models.
Multiscale Model. Simul., 2015
2012
A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations.
Multiscale Model. Simul., 2012
2010
A material frame approach for evaluating continuum variables in atomistic simulations.
J. Comput. Phys., 2010