2020 · Physics
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operators learn mappings between function spaces and can approximate solutions to families of partial differential equations.
Paper & contextAnima Anandkumar
Learning operators and representations for scientific problems.
Anandkumar studies machine learning for scientific and mathematical problems. Fourier neural operators anchor this selection, followed by work on learning mappings between functions, physical systems, and scientific computation.
Selected work
10 papers
2026 · AI for science
Stable Singularity of the Euler Equations on $\mathbb{R}^3$
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Paper & context2026 · AI for science
Stability Framework for the Singularity of the Euler Equations on $\mathbb{R}^3$
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Paper & context2026 · AI for science
Equation Recast for Canonical Operator Learning Across Parametric PDEs
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Paper & context2026 · AI for science
SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning
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Paper & context2026 · AI for science
Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
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Paper & context2026 · AI for science
Inverse Design of Quantum Control Sequences with Fourier Neural Operators
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Paper & context2026 · AI for science
M+Adam: Low-Precision Training via Additive-Multiplicative Optimization
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Paper & context2026 · AI for science
ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers
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Paper & context2026 · AI for science
Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior
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