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Found 7 papers, 2 papers with code
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics
This topic encompasses a broad array of methods and models aimed at solving a single or a collection of PDE problems, called multitask learning.
Neural Operator Learning for Ultrasound Tomography Inversion
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E).
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
This problem is also found in, and in some sense more difficult, with domain decomposition strategies such as temporal decomposition using XPINNs.
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applications.
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
However, the performance of multi-domain PINNs is sensitive to the choice of the interface conditions.
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering (CS&E) world.
BIG-bench Machine Learning
Physics-informed machine learning
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Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Candidates for this approach are simulation methodologies for which there are fidelity differences connected with significant computational cost differences.
