A5103013847- Numerical methods in engineering
- Model Reduction and Neural Networks
- Magnetic Properties and Applications
- Enhanced Oil Recovery Techniques
- Rock Mechanics and Modeling
- Elasticity and Material Modeling
- Seismic Imaging and Inversion Techniques
- Material Properties and Failure Mechanisms
- Fatigue and fracture mechanics
- Composite Material Mechanics
- Hydrocarbon exploration and reservoir analysis
- Lattice Boltzmann Simulation Studies
- CO2 Sequestration and Geologic Interactions
- Non-Destructive Testing Techniques
- Nonlocal and gradient elasticity in micro/nano structures
University of Minnesota
2023-2024
Xiamen University
2022
<title>Abstract</title> The present study aims to extend the novel physics-informed machine learning approach, specifically neural-integrated meshfree (NIM) method, model finite-strain problems characterized by nonlinear elasticity and large deformations. To this end, hyperelastic material models are integrated into loss function of NIM method employing a consistent local variational formulation. Thanks inherent differentiable programming capabilities, can circumvent need for derivation...
The present study aims to extend the novel physics-informed machine learning approach, specifically neural-integrated meshfree (NIM) method, model finite-strain problems characterized by nonlinear elasticity and large deformations. To this end, hyperelastic material models are integrated into loss function of NIM method employing a consistent local variational formulation. Thanks inherent differentiable programming capabilities, can circumvent need for derivation Newton-Raphson linearization...
We present the neural-integrated meshfree (NIM) method, a differentiable programming-based hybrid approach within field of computational mechanics. NIM seamlessly integrates traditional physics-based discretization techniques with deep learning architectures. It employs approximation scheme, NeuroPU, to effectively represent solution by combining continuous DNN representations partition unity (PU) basis functions associated underlying spatial discretization. This neural-numerical...