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 through the degeneracy conditions -- directly in function space. Existing structure-preserving neural operators enforce at most a single conservation law or reversible (Hamiltonian) structure, while thermodynamically consistent learning has been confined to finite-dimensional, graph, or particle systems. GENERIC-FNO clo
The proliferation of complex AI models necessitates more robust and physics-informed architectures to handle real-world challenges, particularly in simulating physical systems accurately.
This development represents a significant step towards more reliable and interpretable AI for scientific and engineering applications, crucial for areas like climate modeling, materials science, and energy systems.
AI models can now embed fundamental thermodynamic principles, potentially leading to more stable, accurate, and generalizable simulations of physical processes without requiring extensive retraining for new conditions.
- · Scientific computing sector
- · Engineering R&D departments
- · Climate modeling research
- · AI model developers
- · Traditional numerical simulation methods
- · AI models lacking structural adherence
Improved accuracy and stability of AI-driven simulations in physics and engineering.
Accelerated discovery of new materials, more efficient energy systems, and better climate predictions.
Enhanced trust in AI for critical infrastructure and scientific research, potentially leading to new breakthroughs in fields previously limited by computational complexity.
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