AI in computational mechanics and engineering sciences

DOI10.1016/j.cma.2023.115935OpenAlexW4320890297MaRDI QIDQ2693415

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Publication date: 20 March 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2023.115935

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Conference proceedings and collections of articles (00Bxx) Quantum theory (81-XX)

Cites Work

• Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold
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• Stochastic nonlocal damage analysis by a machine learning approach
• Deep generative modeling for mechanistic-based learning and design of metamaterial systems
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• Modeling, simulation and machine learning for rapid process control of multiphase flowing foods
• Data-driven learning of nonlocal physics from high-fidelity synthetic data
• Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction
• Diffusion maps-aided neural networks for the solution of parametrized PDEs
• Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares
• Non-invasive inference of thrombus material properties with physics-informed neural networks
• Solving inverse problems in stochastic models using deep neural networks and adversarial training

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