Search Results for author:
Found 14 papers, 5 papers with code
LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures
This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures.
Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators
With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5. 3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for robotics applications.
An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems.
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs).
Constitutive model characterization and discovery using physics-informed deep learning
Additionally, in their discretized form, they are computationally very efficient, often resulting in a simple algebraic relation, and therefore they have been extensively used within large-scale explicit and implicit finite element models.
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE).
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial differential equations (PDEs).
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
We explore an application of the Physics Informed Neural Networks (PINNs) in conjunction with Airy stress functions and Fourier series to find optimal solutions to a few reference biharmonic problems of elasticity and elastic plate theory.
Deep learning for solution and inversion of structural mechanics and vibrations
Deep learning has been the most popular machine learning method in the last few years.
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave.
Energy-based error bound of physics-informed neural network solutions in elasticity
An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems.
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
The performance of existing PINN approaches, however, may degrade in the presence of sharp gradients, as a result of the inability of the network to capture the solution behavior globally.
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks.
A deep learning framework for solution and discovery in solid mechanics
We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training.
