[HTML][HTML] Multifidelity deep operator networks for data-driven and physics-informed problems
Operator learning for complex nonlinear systems is increasingly common in modeling multi-
physics and multi-scale systems. However, training such high-dimensional operators …
physics and multi-scale systems. However, training such high-dimensional operators …
[PDF][PDF] Multifidelity deep operator networks
AA Howard, M Perego… - arxiv preprint arxiv …, 2022 - app.icerm.brown.edu
Multifidelity Deep Operator Networks Page 1 Multifidelity Deep Operator Networks Amanda
Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …
Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …
[HTML][HTML] Prospects of multi-paradigm fusion methods for fluid mechanics research
Experimental observation, theoretical research and numerical simulation are the basic
research paradigms in many disciplines, including fluid mechanics. Since the 21st century …
research paradigms in many disciplines, including fluid mechanics. Since the 21st century …
PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model
To plan and optimize energy storage demands that account for Li-ion battery aging
dynamics, techniques need to be developed to diagnose battery internal states accurately …
dynamics, techniques need to be developed to diagnose battery internal states accurately …
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Although deep learning has achieved remarkable success in various scientific machine
learning applications, its opaque nature poses concerns regarding interpretability and …
learning applications, its opaque nature poses concerns regarding interpretability and …
Transfer learning for flow reconstruction based on multifidelity data
Reduced-order modeling for multifidelity flow reconstruction offers increased accuracy while
saving cost in data generation. The key to obtaining successful multifidelity models lies in …
saving cost in data generation. The key to obtaining successful multifidelity models lies in …
Bi-fidelity modeling of uncertain and partially unknown systems using deeponets
Recent advances in modeling large-scale complex physical systems have shifted research
focuses towards data-driven techniques. However, generating datasets by simulating …
focuses towards data-driven techniques. However, generating datasets by simulating …
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Operator learning frameworks, because of their ability to learn nonlinear maps between two
infinite dimensional functional spaces and utilization of neural networks in doing so, have …
infinite dimensional functional spaces and utilization of neural networks in doing so, have …
Bi-fidelity variational auto-encoder for uncertainty quantification
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary
objective in model validation. However, achieving this goal entails balancing the need for …
objective in model validation. However, achieving this goal entails balancing the need for …
Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets
Recent advances in modeling large-scale, complex physical systems have shifted research
focuses towards data-driven techniques. However, generating datasets by simulating …
focuses towards data-driven techniques. However, generating datasets by simulating …