Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs | ScienceToStartup | ScienceToStartup

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References (12)

[1]

Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation

2025Akshay Govind Srinivasan, Vikas Dwivedi et al.

[2]

ADVANCED NUMERICAL METHODS FOR SOLVING NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS IN FLUID MECHANICS: APPLICATIONS IN AEROSPACE ENGINEERING

2025D. M. Madhavi, Mir Sohail et al.

[3]

Founder's Pitch

"A probabilistic framework improving the efficiency of Physics-Informed Extreme Learning Machines for modeling stiff PDEs."

Scientific Machine Learning•Score: 1•View PDF ↗

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Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs

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References (12)

Founder's Pitch

"A probabilistic framework improving the efficiency of Physics-Informed Extreme Learning Machines for modeling stiff PDEs."

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Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs

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References (12)

Founder's Pitch

"A probabilistic framework improving the efficiency of Physics-Informed Extreme Learning Machines for modeling stiff PDEs."

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