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State of Scientific Computing | Report | ScienceToStartup

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Scientific Computing

State of Scientific Computing

5 papers · avg viability 5.4

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Top papers

Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling(8.0)
OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference(7.0)
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing(5.0)
NeuraLSP: An Efficient and Rigorous Neural Left Singular Subspace Preconditioner for Conjugate Gradient Methods(5.0)
Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning(4.0)
Gauge-Equivariant Intrinsic Neural Operators for Geometry-Consistent Learning of Elliptic PDE Maps(2.0)