Towards out of distribution generalization for problems in mechanics
From MaRDI portal
Publication:2083180
DOI10.1016/j.cma.2022.115569OpenAlexW4283778271MaRDI QIDQ2083180
Lingxiao Yuan, Emma Lejeune, Harold S. Park
Publication date: 10 October 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.14917
Artificial neural networks and deep learning (68T07) Mathematical modeling or simulation for problems pertaining to mechanics of deformable solids (74-10)
Uses Software
Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Causal inference using invariant prediction: identification and confidence intervals
- Automated solution of differential equations by the finite element method. The FEniCS book
- Learning models with uniform performance via distributionally robust optimization
- Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
- Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures
- Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach
- A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing
- A feed-forwarded neural network-based variational Bayesian learning approach for forensic analysis of traffic accident
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Invariance, causality and robustness
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
- A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
- Deformation of FCC nanowires by twinning and slip
- DOLFIN
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Data-driven discovery of coordinates and governing equations
- High–Reynolds Number Wall Turbulence
- Causality for Machine Learning
