Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains
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Publication:2072515
DOI10.1016/j.cma.2021.114424OpenAlexW3205765690WikidataQ114196861 ScholiaQ114196861MaRDI QIDQ2072515
Aparna Chandramowlishwaran, Hengjie Wang, Robert Planas, Ramin Bostanabad
Publication date: 26 January 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.10873
Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)
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Multi-Fidelity Machine Learning Applied to Steady Fluid Flows ⋮ Long-time integration of parametric evolution equations with physics-informed DeepONets ⋮ Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems ⋮ Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases ⋮ Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps ⋮ A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty ⋮ A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients
Uses Software
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