From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators | ScienceToStartup | ScienceToStartup

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References (31)

[1]

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries

2024Zhihao Li, Haoze Song et al.

[2]

M2NO: An Efficient Multi-Resolution Operator Framework for Dynamic Multi-Scale PDE Solvers

2024Zhihao Li, Zhilu Lai et al.

[3]

Latent Neural Operator for Solving Forward and Inverse PDE Problems

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"Develop a Gaussian Particle Operator for interpretable and efficient PDE simulations with near-linear complexity and mesh-agnostic geometry."

Interpretable AI•Score: 5•View PDF ↗

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From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators

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References (31)

Founder's Pitch

"Develop a Gaussian Particle Operator for interpretable and efficient PDE simulations with near-linear complexity and mesh-agnostic geometry."

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From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators

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References (31)

Founder's Pitch

"Develop a Gaussian Particle Operator for interpretable and efficient PDE simulations with near-linear complexity and mesh-agnostic geometry."

Commercial Viability Breakdown

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