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State of Scientific Machine Learning | Report | ScienceToStartup
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Scientific Machine Learning
State of Scientific Machine Learning
4 papers · avg viability 4.5
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Top papers
Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
(7.0)
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
(6.0)
Physics-informed fine-tuning of foundation models for partial differential equations
(4.0)
Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
(1.0)