Reese E. Jones

Orcid: 0000-0002-2332-6279

According to our database1, Reese E. Jones authored at least 17 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

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
Tensor Basis Gaussian Process Models of Hyperelastic Materials.
CoRR, 2019

2016
Machine learning strategies for systems with invariance properties.
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


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