Phi-ML meets Engineering: Thermodynamics-Informed Neural Network: Transverse Symplectic Foliation Structure of Souriau’s Lie Groups Thermodynamics

Learn more Subscribe to attend Add to Calendar 02/06/2025 01:00 PM 02/06/2025 02:00 PM Europe/London Phi-ML meets Engineering: Thermodynamics-Informed Neural Network: Transverse Symplectic Foliation Structure of Souriau’s Lie Groups Thermodynamics Location of the event
Thursday 06 Feb 2025
Time: 13:00 - 14:00

Event type

Seminar
Free

Introduction

This bi-monthly seminar series explores real-world applications of physics-informed machine learning (Φ-ML) methods to the engineering practice. They cover a wide range of topics, offering a cross-sectional view of the state of the art on Φ-ML research, worldwide.  

Participants have the opportunity to hear from leading researchers and learn about the latest developments in this emerging field. These seminars also offer the chance to identify and spark collaboration opportunities.

About the event

Thermodynamics understanding by Geometric model were initiated by all precursors Carnot, Gibbs, Duhem, Reeb, Carathéodory. It is only recently that Symplectic Foliation Model introduced in the domain of Geometric statistical Mechanics has opened the door for a solid  bedrock, giving a geometric definition of Entropy as invariant Casimir function on Symplectic leaves (coadjoint orbit of the Lie Group acting on the system and interpreted as level sets of Entropy).

As observed by Georges Reeb "Thermodynamics has long accustomed mathematical physics [see DUHEM P.] to the consideration of completely integrable Pfaff forms: the elementary heat dQ [notation of thermodynamicists] representing the elementary heat yielded in an infinitesimal reversible modification is such a completely integrable form. This point does not seem to have been explored since then." Notion of foliation in thermodynamics appears in  C. Carathéodory paper where horizontal curves roughly correspond to adiabatic processes, performed in the language of Carnot cycles.  The properties of the couple of Poisson manifolds was  previously explored by C. Carathéodory in 1935, under the name of “function groups, polar to each other”. This seminal work of C. Caratheodory leads to the concept of a Poisson structure which was first defined independently by Lichnerowicz and  Kirillov.

A symplectic foliation model of Thermodynamics has been defined by Jean-Marie Souriau based on "Lie Groups Thermodynamics" model. This model gives a cohomological characterization of Entropy, as an invariant Casimir function in coadjoint representation. The dual space of the Lie algebra foliates into coadjoint orbits identified with the Entropy level sets. In the framework of Thermodynamics, a symplectic bifoliation structure is associated to describe non-dissipative dynamics on symplectic leaves (on level sets of Entropy as constant Casimir function on each leaf), and transversal dissipative dynamics, given by Poisson transverse structure (Entropy production from leaf to leaf). The symplectic foliation orthogonal to the level sets of moment map is the foliation determined by hamiltonian vector fields generated by functions on dual Lie algebra. The orbits of a Hamiltonian action and the level sets of its moment map are polar to each other. The space of Casimir functions on a neighborhood of a point is isomorphic to the space of Casimirs for the transverse Poisson structure. Souriau’s model could be then interpreted by Libermann's foliations, clarified as dual to Poisson Gamma-structure of Haefliger, which is the maximum extension of the notion of moment in the sense of J.M. Souriau, as introduced by P. Molino, M. Condevaux and P. Dazord in papers of  “Séminaire Sud-Rhodanien de Géométrie ». The symplectic duality to a symplectically complete foliation, in the sense of Libermann, associates an orthogonal foliation. We conclude with link to Cartan foliation and Edmond Fedida works on Cartan's mobile frame-based foliation.

In the first part, we will present the theme "Statistical learning on Lie groups" [1,2] which extends statistics and machine learning to Lie groups based on the theory of representations and cohomology of Lie algebra. From the work of Jean-Marie Souriau on " Lie Groups Thermodynamics" [4] initiated within the framework of symplectic models of statistical mechanics, new geometric statistical tools have been developed to define probability densities (Gibbs density of Maximum Entropy) on Lie Groups or homogeneous manifolds for supervised methods, and the extension of the Fisher metric of Information Geometry for unsupervised methods (in metric spaces).

In a 2nd part, TINNs (Thermodynamics-Informed Neural Networks) [3,5] will be discussed for AI-augmented engineering applications. The geometric structures of TINNs are studied by their metriplectic flow (also called GENERIC flow) modeling non-dissipative dynamics (1st thermodynamic principle of energy conservation) and dissipative dynamics (2nd thermodynamic principle of entropy production). The Thermodynamics of Lie Groups of Souriau makes it possible to geometrically characterize the metriplectic flow by a structure of “webs” composed of symplectic foliations and transversely Riemannian foliations. From the symmetries of the problem, the coadjoint orbits of the Lie group generate via the moment map (geometrization of Noether's theorem) the symplectic foliation (defined as the level sets of entropy, where entropy is an invriant Casimir function on these symplectic leaves). The metric on symplectic leaves is given by the Fisher metric. The dynamics along these symplectic leaves, given by the Poisson bracket, characterizes the non-dissipative dynamics. The dissipative dynamics is then given by the transverse Poisson structure and a metric flow  bracket, with an evolution from leaf to leaf constrained by the production of entropy. The transverse foliation is a Riemannian foliation whose metric is given by the dual of Fisher metric (Hessian of Entropy).

These machine-learning tools on Lie groups and TINNs are addressed in two European projects: a European network COST CaLISTA [6] and the European Marie-Curie action MSCA CaLIGOLA [7].

References:

[1] Barbaresco, F. (2022) Symplectic theory of heat and information geometry, chapter 4, Handbook of Statistics, Volume 46, Pages 107-143, Elsevier, https://doi.org/10.1016/bs.host.2022.02.003 https://www.sciencedirect.com/science/article/abs/pii/S0169716122000062  

[2] Barbaresco, F. (2023). Symplectic Foliation Transverse Structure and Libermann Foliation of Heat Theory and Information Geometry. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14072. Springer, Cham. https://doi.org/10.1007/978-3-031-38299-4_17  ; https://link.springer.com/chapter/10.1007/978-3-031-38299-4_17  

[3] Barbaresco, F. (2022) Symplectic Foliation Structures of Non-Equilibrium Thermodynamics as Dissipation Model: Application to Metriplectic Nonlinear Lindblad Quantum Master Equation. Entropy 2022, 24, 1626. https://doi.org/10.3390/e24111626

[4] Souriau, J.M. (1969). Structure des systèmes dynamiques. Dunod. http://www.jmsouriau.com/structure_des_systemes_dynamiques.htm    

[5] Cueto E. and Chinesta F. (2022), Thermodynamics of Learning of Physical Phenomena, arXiv:2207.12749v1 [cs.LG] 26 Jul 2022

[6] European COST network CA21109 : CaLISTA  - Cartan geometry, Lie, Integrable Systems, quantum group Theories for Applications ; https://site.unibo.it/calista/en

[7] European HORIZON-MSCA-2021-SE-01-01 project CaLIGOLA - Cartan geometry, Lie and representation theory, Integrable Systems, quantum Groups and quantum computing towards the understanding of the geometry of deep Learning and its Applications; https://site.unibo.it/caligola/en

[8] Barbaresco, F.  (2021) Koszul lecture related to geometric and analytic mechanics, Souriau’s Lie group thermodynamics and information geometry. Info. Geo. 4, 245–262. https://doi.org/10.1007/s41884-020-00039-x

[9]  Barbaresco, F. (2021). Gaussian Distributions on the Space of Symmetric Positive Definite Matrices from Souriau’s Gibbs State for Siegel Domains by Coadjoint Orbit and Moment Map. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_28

[10] Barbaresco, F. (2021). Koszul Information Geometry, Liouville-Mineur Integrable Systems and Moser Isospectral Deformation Method for Hermitian Positive-Definite Matrices. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_39

[11] Barbaresco, F. (2021). Jean-Marie Souriau’s Symplectic Model of Statistical Physics: Seminal Papers on Lie Groups Thermodynamics - Quod Erat Demonstrandum. In: Barbaresco, F., Nielsen, F. (eds) Geometric Structures of Statistical Physics, Information Geometry, and Learning. SPIGL 2020. Springer Proceedings in Mathematics & Statistics, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-030-77957-3_2

[12] 7th SEE Geometic Science of Information; https://conference-gsi.org/

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