Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
Results and benchmarks
Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks presents a optimization problem approach for artificial neural network.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 60/100, grounding 58/100, status medium.
Implementation
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Time to first repro: a few hours
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Reproduction readiness
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Validation caveat
Hugging Face artifacts
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Research context
86
Citations
61
References
Tasks
Artificial neural network, Computer science, Deep learning, Physical Sciences
Methods
Optimization problem, Surrogate model, Mathematical optimization, Meta-optimization, Algorithm, Optimization algorithm
Domains
Artificial intelligence, Machine learning, Physics and Astronomy, Statistical and Nonlinear Physics
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