Intelligent computing: the latest advances, challenges, and future
Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
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 …
[HTML][HTML] NN-EUCLID: Deep-learning hyperelasticity without stress data
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
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 …
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …
with the prospect of significantly relieving the computational costs of hierarchical …
Modular machine learning-based elastoplasticity: Generalization in the context of limited data
The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …
dependent processes continues to be a complex challenge in computational solid …
Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
Hierarchical computational methods for multiscale mechanics such as the FE 2 and FE-FFT
methods are generally accompanied by high computational costs. Data-driven approaches …
methods are generally accompanied by high computational costs. Data-driven approaches …
[HTML][HTML] Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties
Within the scope of our recent approach for Efficient Unsupervised Constitutive Law
Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning …
Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning …
Modeling the solid electrolyte interphase: Machine learning as a game changer?
The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
Model-data-driven constitutive responses: Application to a multiscale computational framework
Computational multiscale methods for analyzing and deriving constitutive responses have
been used as a tool in engineering problems because of their ability to combine information …
been used as a tool in engineering problems because of their ability to combine information …