[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …
to the widespread digital data, growing computing power, and advanced algorithms. The …
Artificial intelligence and sentiment analysis: A review in competitive research
As part of a business strategy, effective competitive research helps businesses outperform
their competitors and attract loyal consumers. To perform competitive research, sentiment …
their competitors and attract loyal consumers. To perform competitive research, sentiment …
[HTML][HTML] A new family of Constitutive Artificial Neural Networks towards automated model discovery
For more than 100 years, chemical, physical, and material scientists have proposed
competing constitutive models to best characterize the behavior of natural and man-made …
competing constitutive models to best characterize the behavior of natural and man-made …
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …
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 …
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems
MJ Buehler - Journal of the Mechanics and Physics of Solids, 2023 - Elsevier
We report a flexible multi-modal mechanics language model, MeLM, applied to solve
various nonlinear forward and inverse problems, that can deal with a set of instructions …
various nonlinear forward and inverse problems, that can deal with a set of instructions …