Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element …
Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …
boundary value problem. Here, the training of the network is equivalent to the minimization …
Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance
Physics-informed neural networks (PINN) can solve partial differential equations (PDEs) by
encoding the mathematical information explicitly into the loss functions. In the context of …
encoding the mathematical information explicitly into the loss functions. In the context of …
Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads
Abstract Deep Operator Network (DeepONet), a recently introduced deep learning operator
network, approximates linear and nonlinear solution operators by taking parametric …
network, approximates linear and nonlinear solution operators by taking parametric …
Deep learning operator network for plastic deformation with variable loads and material properties
The advent of data-driven and physics-informed neural networks has sparked interest in
deep neural networks as universal approximators of solutions in various scientific and …
deep neural networks as universal approximators of solutions in various scientific and …
A deep learning energy-based method for classical elastoplasticity
The deep energy method (DEM) has been used to solve the elastic deformation of structures
with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on …
with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on …
Enhanced physics‐informed neural networks for hyperelasticity
DW Abueidda, S Koric, E Guleryuz… - International Journal for …, 2023 - Wiley Online Library
Physics‐informed neural networks have gained growing interest. Specifically, they are used
to solve partial differential equations governing several physical phenomena. However …
to solve partial differential equations governing several physical phenomena. However …
Integrated Finite Element Neural Network (I-FENN) for non-local continuum damage mechanics
P Pantidis, ME Mobasher - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
We present a new Integrated Finite Element Neural Network framework (I-FENN), with the
objective to accelerate the numerical solution of nonlinear computational mechanics …
objective to accelerate the numerical solution of nonlinear computational mechanics …