- Open Access
Hebbian Physics Networks: A self-organizing computational architecture based on local physical laws
Phys. Rev. Research 8, 013309 – Published 23 March, 2026
DOI: https://doi.org/10.1103/tzgk-jqj4
Abstract
Physical transport processes organize through local interactions that redistribute imbalance while preserving conservation. Classical solvers enforce this organization by applying fixed discrete operators on rigid grids. We introduce the Hebbian Physics Network (HPN), a computational framework that replaces this rigid scaffolding with a plastic transport geometry. An HPN is a coupled dynamical system of physical states on nodes and constitutive weights on edges in a graph. Residuals, local violations of continuity, momentum balance, or energy conservation, act as thermodynamic forces that drive the joint evolution of both the state and the operator (i.e., the adaptive weights). The weights adapt through a residual-modulated anti-Hebbian rule, which we prove constitutes a strictly local gradient descent on the residual energy. This mechanism ensures thermodynamic consistency: Near equilibrium, the realized transport operator converges to a symmetric, positive-definite form, reproducing Onsager's reciprocal relations without explicit enforcement. Far from equilibrium, the system self-organizes into transport topologies within a thermodynamically admissible class that redistribute imbalance through adaptive local geometry. Unlike optimization-based approaches that impose physics through global loss functions, HPNs embed conservation intrinsically: Transport is restored locally by the evolving operator itself, without a global Poisson solve or backpropagated objective. We demonstrate the framework on scalar diffusion and incompressible lid-driven cavity flow, showing that physically consistent transport geometries and flow structures emerge from random initial conditions solely through residual-driven local adaptation. HPNs thus reframe computation not as the solution of a fixed equation, but as a thermodynamic relaxation process in which the constitutive geometry and physical state coevolve.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (36)
- S. Patankar, Numerical Heat Transfer and Fluid Flow (CRC Press, New York, 2018).
- J. H. Ferziger, M. Perić, and R. L. Street, Computational Methods for Fluid Dynamics (Springer, 2019).
- R. J. LeVeque, Finite Volume Methods for Hyperbolic Problems (Cambridge University Press, 2002), Vol. 31.
- J. Boussinesq, Essai Sur la Théorie Des Eaux Courantes (Imprimerie Nationale, 1877).
- S. B. Pope, Turbulent flows, Meas. Sci. Technol. 12, 2020 (2001).
- S. R. De Groot and P. Mazur, Non-Equilibrium Thermodynamics (Courier Corporation, 2013).
- M. Z. Bazant, M. S. Kilic, B. D. Storey, and A. Ajdari, Towards an understanding of induced-charge electrokinetics at large applied voltages in concentrated solutions, Adv. Colloid Interface Sci. 152, 48 (2009).
- J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA 79, 2554 (1982).
- Predicting Structured Data, edited by G. BakIr, T. Hoffman, B. Schölkopf, S. Bernhard, J. Alexander, B. Taskar, and S. V. N. Vishwanathan (MIT press, Cambridge, Massachusetts, 2007).
- K. Friston, The free-energy principle: A unified brain theory? Nat. Rev. Neurosci. 11, 127 (2010).
- A. J. Chorin, A numerical method for solving incompressible viscous flow problems, J. Comput. Phys. 135, 118 (1997).
- A. Jameson, Time dependent calculations using multigrid, with applications to unsteady flows past airfoils and wings, in Proceedings of the 10th Computational Fluid Dynamics Conference (Honolulu HI USA, American Institute of Aeronautics and Astronautics, 1991).
- U. Frisch, B. Hasslacher, and Y. Pomeau, Lattice-gas automata for the Navier-Stokes equation, Phys. Rev. Lett. 56, 1505 (1986).
- G. G. McNamara and G. Zanetti, Use of the Boltzmann equation to simulate lattice-gas automata, in Lattice Gas Methods for Partial Differential Equations (CRC Press, 2019), pp. 289–296.
- S. Wolfram, Cellular Automata and Complexity Collected Papers, Part one (CRC Press, Boca Raton, Florida, 2018).
- L. Onsager, Reciprocal relations in irreversible processes. I., Phys. Rev. 37, 405 (1931).
- H. B. Callen, Thermodynamics and an Introduction to Thermostatistics, 2nd ed. (John Wiley & Sons, 1993).
- I. Prigogine and G. Nicolis, Self-Organization in Non-Equilibrium Systems (Wiley, 1977).
- I. Prigogine, Time, structure, and fluctuations, Science 201, 777 (1978).
- M. C. Cross and P. C. Hohenberg, Pattern formation outside of equilibrium, Rev. Mod. Phys. 65, 851 (1993).
- P. Földiak, Forming sparse representations by local anti-Hebbian learning, Biol. Cybern. 64, 165 (1990).
- N. Frémaux and W. Gerstner, Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules, Front. Neural Circuits 9, 85 (2016).
- T. Isomura and T. Toyoizumi, Error-gated Hebbian rule: A local learning rule for principal and independent component analysis, Sci. Rep. 8, 1835 (2018).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/tzgk-jqj4 for additional information on the demonstrative cases, which includes Ref. [25] as benchmarking data.
- U. Ghia, K. N. Ghia, and C. Shin, High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method, J. Comput. Phys. 48, 387 (1982).
- M. Raissi, A. Yazdani, and G. E. Karniadakis, Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations, Science 367, 1026 (2020).
- J. L. Cardona, K. L. Bouman, and J. O. Dabiri, Wind speed inference from environmental flow–structure interactions, Flow 1, E4 (2021).
- G. Lebon, D. Jou, and J. Casas-Vázquez, Understanding Non-Equilibrium Thermodynamics: Foundations, Applications, Frontiers (Springer, Berlin, Heidelberg, 2008).
- W. G. Hoover, Computational Statistical Mechanics (Elsevier, Amsterdam, Netherlands, 2012).
- D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, A learning algorithm for Boltzmann machines, Cognit. Sci. 9, 147 (1985).
- C. W. Misner, K. S. Thorne, and J. A. Wheeler, Gravitation (WH Freeman and company San Francisco, 1973).
- V. I. Arnold, Geometrical Methods in the Theory of Ordinary Differential Equations (Springer Science & Business Media, New York, 2012), Vol. 250.
- F. W. Hehl, P. Von der Heyde, G. D. Kerlick, and J. M. Nester, General relativity with spin and torsion: Foundations and prospects, Rev. Mod. Phys. 48, 393 (1976).
- M. Nakahara, Geometry, Topology and Physics (Institute of Physics Publishing, Bristol, 2003).
- S.-I. Amari, Natural gradient works efficiently in learning, Neural Comput. 10, 251 (1998).
- J. E. Marsden and T. S. Ratiu, Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems (Springer, New York, 2013), Vol. 17.