Reese E. Jones
- Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
2023/10/05 by Jan N. Fuhg, Reese E. Jones, Fuhg, Jan N. +3 · 8 citations
Physics and Astronomy · Engineering · Medicine · #Model Reduction and Neural Networks #Elasticity and Material Modeling #Orthopaedic implants and arthroplasty
- Predicting the mechanical response of oligocrystals with deep learning
2019/01/30 by Ari Frankel, Frankel, Ari L., Reese E. Jones +5 · 2 citations
Engineering · Materials Science · #Composite Material Mechanics #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Microstructure and mechanical properties
- Polyconvex neural network models of thermoelasticity
2024/04/23 by Jan N. Fuhg, Fuhg, Jan N., Asghar Jadoon +7 · 6 citations
Engineering · #Computational Physics (physics.comp-ph) #Elasticity and Wave Propagation #FOS: Physical sciences #Radiative Heat Transfer Studies #Soft Condensed Matter (cond-mat.soft) #Thermoelastic and Magnetoelastic Phenomena
- Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
2023/08/21 by Jan N. Fuhg, Nikolaos Bouklas, Fuhg, Jan N. +3 · 2 citations
Engineering · Mathematics · Physics and Astronomy · #Elasticity and Material Modeling #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Soft Condensed Matter (cond-mat.soft) #Tensor decomposition and applications
- An attention-based neural ordinary differential equation framework for modeling inelastic processes
2025/02/15 by Reese E. Jones, Jones, Reese E., Jan N. Fuhg +1 · 6 citations
Computer Science · #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications
- Prediction of the evolution of the stress field of polycrystals\n undergoing elastic-plastic deformation with a hybrid neural network model
2019/10/07 by Ari Frankel, Kousuke Tachida, Frankel, Ari +3 · 1 citation
Engineering · Materials Science · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Hydrogen embrittlement and corrosion behaviors in metals #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Microstructure and Mechanical Properties of Steels
- Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
2022/09/27 by Ruben Villarreal, Villarreal, Ruben, Nikolaos N. Vlassis +13 · 1 citation
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design
- Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
2024/06/30 by Govinda Anantha Padmanabha, Jan N. Fuhg, Padmanabha, Govinda Anantha +7 · 2 citations
Physics and Astronomy · Computer Science · Earth and Planetary Sciences · #Model Reduction and Neural Networks #Domain Adaptation and Few-Shot Learning #Seismic Imaging and Inversion Techniques
- Input Specific Neural Networks
2025/03/01 by Asghar Jadoon, Daniel Seidl, Jadoon, Asghar A. +5 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #and Science (cs.CE)
- Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
2026/07/22 by Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan +4
Computer Science · Physics and Astronomy · #cs.LG #physics.comp-ph