Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
arXiv:2606.01179v1 Announce Type: new Abstract: Entropy production governs irreversibility and uncertainty in both physical and information-theoretic systems. While Physics-Informed Neural Networks (PINNs) successfully solve differential equations, current architectures remain inherently domain-specific. The extraction of domain-invariant entropy representations across fundamentally different physical laws remains unexplored. This paper introduces a unified Physics-Informed Deep Learning (PIDL) framework that simultaneously enforces differential equation residuals and information-theoretic bou
The rapid advancements in deep learning compel researchers to seek more generalized and physically grounded AI architectures, particularly for complex system simulations. The increasing demand for robust and interpretable AI in scientific discovery drives the development of frameworks like PIDL.
This research introduces a novel, unified framework for Physics-Informed Deep Learning, capable of extracting domain-invariant entropy representations, which can significantly enhance the predictive capabilities and generalizability of AI in diverse scientific and engineering applications.
The ability to model complex systems with deep learning, integrating both physical laws and information theory, will become more robust and less domain-specific, potentially accelerating scientific discovery and engineering design processes.
- · AI researchers (physics-informed AI)
- · Computational physicists
- · Materials science
- · Drug discovery
- · Traditional simulation methods (in specific applications)
- · AI models lacking physical constraints
More accurate and efficient simulations of complex physical and information-theoretic systems will be possible.
This improved simulation capability could lead to faster development cycles for new materials, drugs, and engineering solutions.
The generalized understanding of entropy across domains might unlock completely new theoretical insights in physics and information science, fostering further AI breakthroughs.
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