An Entropy-Driven Physical Automaton conceptual Model on Static Graphs with Resonance-Encoded States -Enabling AI for Science.
This model proposes a preparatory framework for a physics simulator based on a stable graph structure.
(1) By fixing the graph topology, the system’s complexity is simplified while global energy conservation is maintained.
(2) A novel computable entropy is introduced to assign an entropy value to each state update, serving as a time-entropy coordinate.
(3) The tensor variations between lattice sites in general relativity are represented through the oscillation frequencies of quantum nodes, greatly simplifying numerical simulations of curved spacetime.
(4) Due to the stability of the graph structure, each quantum node has fixed spatial coordinates and only two degrees of freedom: resonant frequency and orientation of the oscillation axis. Quantum spin dynamics can be effectively described by such harmonic oscillations, leading to a significantly simplified Hamiltonian form for simulating quantum field evolution.
(5) The model’s two-layer foundation allows the coexistence of metric degrees of freedom from general relativity and spin-axis-oscillation degrees of freedom from quantum field theory.
(6) Matter is defined as excitation waves that preserve the topological structure of space itself—an assumption that leaves room for future extensions.
(7) The update rules governing the model are simple and visually intuitive, primarily driven by thermodynamic principles.
(8) The model aligns with a wide range of known physical phenomena and behaves consistently with the real world.
(9) This model significantly reduces the computational complexity of numerical relativity through the postulate of a "metric-quantum frequency mirroring" principle. By leveraging a derived metric-frequency correspondence (ω_QFT = f(g_μν)_GR) from cosmological gravitational redshift and QCD-level interactions, it enables simulations of general relativistic phenomena without resorting to traditional Riemannian geometric calculations—instead mapping the metric tensor structure directly onto the quantum frequency domain.The central objective becomes the construction of a "metric-quantum frequency spectrum," offering an alternative, frequency-based framework for modeling spacetime dynamics. This approach not only simplifies the mathematical formalism but also provides a physically intuitive and computationally tractable tool for numerical relativity, with potential applications in bridging quantum field theory and gravity at both theoretical and observational levels.
By embedding the thermodynamic arrow of time, this framework provides an intrinsic entropy-based coordinate for system evolution. It is capable of simulating aspects of both general relativity and quantum field theory. If further developed and adopted, it may offer an alternative foundational engine for digital physics simulations.
Keywords:Physical Automata; Space-Time-Entropy Mapping;Discrete Spacetime;Thermodynamic Time Arrow; Multiplicative Entropy; Space Elementary Quanta(SEQ); Mass-Gravity-SU(3) mechanism; Higgs chiral lock; Analytic Quantum Thermodynamic; Quantum Gravity;Static Graphs
In recent years, both the scientific community and industry have increasingly recognized that artificial intelligence is not merely a tool for automation, but a transformative paradigm for discovering new science. However, realizing this potential requires more than statistical learning — it demands a foundational simulation engine capable of emulating the genuine dynamics of the physical world. Such an engine should not simply solve known equations numerically, but function as a meta-driving platform — a substrate where space, time, matter, and interactions can co-evolve in a self-consistent manner. We envision a new path to scientific discovery: using AI to simulate and explore self-evolving systems driven by minimal, local rules.
At the heart of this vision lies thermodynamics. The evolution of the universe is fundamentally driven by entropy increase, and the arrow of time is not a mere mathematical parameter, but an intrinsic manifestation of state change within the system. Therefore, an ideal physical simulator must embed the thermodynamic arrow of time directly into its update mechanism, using computable, non-statistical thermodynamic quantities to characterize each evolutionary step. In other words, we do not wish to analyze entropy changes afterward — we want entropy to serve as a coordinate during evolution.
Existing simulation approaches — whether based on continuous differential geometry or discrete lattice field theories — have achieved great success in specific domains, yet face structural challenges in unifying general relativity with quantum field theory: spacetime dynamics and quantum degrees of freedom struggle to coexist; time plays inconsistent roles across theories; and thermodynamics is often introduced as a secondary property, rather than a primary driver of change.
This paper proposes a preparatory physical simulation framework based on a stable graph structure, offering a novel pathway toward such unification. The model simplifies complexity through a fixed graph topology while preserving global energy conservation. A newly defined computable entropy function assigns an entropy value at every update step, forming a “time-entropy” coordinate system that intrinsically embeds irreversibility. Tensor-like variations analogous to those in general relativity are encoded via spatial gradients in node oscillation frequencies, enabling efficient approximation of curvature effects without explicit metric solving. Each quantum node has only two degrees of freedom — resonant frequency (energy) and orientation of the oscillation axis (spin-like), allowing a simplified Hamiltonian description of quantum field evolution. A two-layer architecture ensures the coexistence of geometric and quantum variables. Matter is modeled as topologically stable excitation waves propagating over the space substrate itself — a first step toward the idea that "matter is geometry in motion." The entire system evolves under simple, locally defined, thermodynamically motivated rules, with clear visual interpretability. Rather than aiming at high-precision fitting of specific phenomena, this framework seeks to become a foundational simulation engine — one in which physical laws may gradually emerge from minimal, deterministic, entropy-driven update rules. If further developed, it could provide a new computational foundation for digital physics, AI-driven scientific discovery, and unified modeling of nature.
The Full text of this Model: Zou, Z. K. (2025). Time-Entropy Mapping; Mass-Gravity Duality; Metric-Frequency Mirroring—A Two-Layer Fiber Bundle Model with Topologically Invariant Space Configuration. Preprints. https://doi.org/10.20944/preprints202505.0270.v10