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GENERIC-FNO embeds thermodynamics into neural operators

Researchers have developed GENERIC-FNO, a novel neural operator designed to embed the principles of nonequilibrium thermodynamics directly into function space. This model uniquely integrates reversible, energy-conserving dynamics with irreversible, entropy-producing dynamics, a feat not previously achieved in neural operators. GENERIC-FNO learns energy and entropy functionals and enforces exact structural guarantees, demonstrating high precision and outperforming existing baselines on various physical dynamics. AI

IMPACT Advances fundamental AI capabilities for simulating complex physical systems with guaranteed thermodynamic consistency.

RANK_REASON The cluster contains a research paper detailing a new model architecture for neural operators.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jason Sulskis, Sathya Ravi ·

    GENERIC-FNO: Embedding Energy Conservation and Entropy Production into Fourier Neural Operators

    arXiv:2606.08343v1 Announce Type: new Abstract: We introduce GENERIC-FNO, the first neural operator to embed the full GENERIC (metriplectic) structure of nonequilibrium thermodynamics -- reversible, energy-conserving dynamics and irreversible, entropy-producing dynamics coupled t…

  2. arXiv cs.LG TIER_1 English(EN) · Sathya Ravi ·

    GENERIC-FNO: Embedding Energy Conservation and Entropy Production into Fourier Neural Operators

    We introduce GENERIC-FNO, the first neural operator to embed the full GENERIC (metriplectic) structure of nonequilibrium thermodynamics -- reversible, energy-conserving dynamics and irreversible, entropy-producing dynamics coupled through the degeneracy conditions -- directly in …