[HTML][HTML] Battery safety: Machine learning-based prognostics
Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …
devices to large-scale electrified transportation systems and grid-scale energy storage …
Recent advances and applications of machine learning in experimental solid mechanics: A review
H Jin, E Zhang, HD Espinosa - Applied …, 2023 - asmedigitalcollection.asme.org
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Physics-informed deep neural operator networks
Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
An expert's guide to training physics-informed neural networks
Physics-informed neural networks (PINNs) have been popularized as a deep learning
framework that can seamlessly synthesize observational data and partial differential …
framework that can seamlessly synthesize observational data and partial differential …
Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …
(PINNs) has been proposed and gained prevalence in solving various scientific computing …
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 …
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
Multiscale modeling is an effective approach for investigating multiphysics systems with
largely disparate size features, where models with different resolutions or heterogeneous …
largely disparate size features, where models with different resolutions or heterogeneous …
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 …
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 …