Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Physics informed neural networks for continuum micromechanics
Recently, physics informed neural networks have successfully been applied to a broad
variety of problems in applied mathematics and engineering. The principle idea is the usage …
variety of problems in applied mathematics and engineering. The principle idea is the usage …
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 …
Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity
The paper presents an efficient and robust data-driven deep learning (DL) computational
framework developed for linear continuum elasticity problems. The methodology is based on …
framework developed for linear continuum elasticity problems. The methodology is based on …
A physics-informed neural network-based topology optimization (PINNTO) framework for structural optimization
Abstract Physics-Informed Neural Networks (PINNs) have recently attracted exponentially
increasing attention in the field of computational mechanics. This paper proposes a novel …
increasing attention in the field of computational mechanics. This paper proposes a novel …
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 …
framework for forward, inverse, and surrogate modeling of problems governed by partial …
A data-driven physics-constrained deep learning computational framework for solving von Mises plasticity
Current work presents an efficient data-driven Physics Informed Neural Networks (PINNs)
computational framework for the solution of elastoplastic solid mechanics. To incorporate …
computational framework for the solution of elastoplastic solid mechanics. To incorporate …
[HTML][HTML] A complete physics-informed neural network-based framework for structural topology optimization
Abstract Physics-Informed Neural Networks (PINNs) have recently gained increasing
attention in the field of topology optimization. The fusion of deep learning and topology …
attention in the field of topology optimization. The fusion of deep learning and topology …
A PINN-based modelling approach for hydromechanical behaviour of unsaturated expansive soils
Hydromechanical behaviour of unsaturated expansive soils is complex, and current
constitutive models failed to accurately reproduce it. Different from conventional modelling …
constitutive models failed to accurately reproduce it. Different from conventional modelling …
A gentle introduction to physics-informed neural networks, with applications in static rod and beam problems
D Katsikis, AD Muradova… - Journal of Advances in …, 2022 - avantipublisher.com
A modern approach to solving mathematical models involving differential equations, the so-
called Physics-Informed Neural Network (PINN), is based on the techniques which include …
called Physics-Informed Neural Network (PINN), is based on the techniques which include …