We set forth a definition of hyperfinite knots. Loosely speaking, these are limits of certain sequences of knots with increasing crossing number. These limits exist in appropriate closures of quotient spaces of knots. We give examples of hyperfinite knots. These examples stem from an application of the Thermodynamic Li…
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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48 results for “thermodynamical limit”
Develops thermodynamic formalism for quasimorphisms on negatively curved spaces.
problem Analyzing quasimorphisms on negatively curved spaces.
method Thermodynamic formalism framework, Banach isomorphism, weak Livšic cohomology.
result Establishes Central Limit Theorem and invariance principle for unbounded quasimorphisms.
Caratheodory's axiom limits arbitrage in resource-limited systems.
problem Non-arbitrage constraints in resource-limited financial systems.
method Preserving Caratheodory's axiom in resource-limited systems.
result Exponential family is the necessary geometric structure for both thermodynamics and finance.
We show how the smooth geometry of Calabi-Yau manifolds emerges from the thermodynamic limit of the statistical mechanical model of crystal melting defined in our previous paper arXiv:0811.2801. In particular, the thermodynamic partition function of molten crystals is shown to be equal to the classical limit of the par…
Economic Cycles of Carnot Typeq-fin.GN
Paper introduces economic Carnot cycles in Roegenian economics.
problem Economic efficiency limits in Roegenian systems.
method Uses Carnot cycle principles to model economic systems.
result Validates economic Carnot cycles in Roegenian economics.
Learning three data points can generate all types of periodic orbits in a neural network.
problem Can learning three data points generate all types of periodic orbits in a neural network?
method Investigated a continuous one-dimensional map with period three in a random neural network in its thermodynamic limit.
result Almost all learned periods are unstable, and each network has its own characteristic attractors.
Unified thermodynamic approach to Transformer attention dynamics.
problem Understanding the statistical mechanics of Transformer attention.
method Constructing a Lagrangian on the information manifold to analyze attention dynamics.
result Establishes a formal correspondence between scaled dot-product attention and canonical ensemble statistics.
The formal structure of geometrical thermodynamics is reviewed with particular emphasis on the geometry of equilibria submanifolds. On these submanifolds thermodynamic metrics are defined as the Hessian of thermodynamic potentials. Links between geometry and thermodynamics are explored for single and multiple component…
Minimum-Excess-Work Guidancestat.ML
Proposes a thermodynamic work minimization framework for guiding generative models.
problem Guiding generative models in sparse-data regimes with limited target samples or constraints.
method Regularization framework inspired by thermodynamic work, introducing Path Guidance and Observable Guidance.
result Improves sample efficiency and reduces bias in molecular simulations.
Thermodynamics of marketsq-fin.GN
The thermodynamics of markets is analyzed, revealing parallels with thermodynamic laws.
problem Understanding economic systems through thermodynamic principles.
method Derivation and analysis of thermodynamic laws applied to economic systems.
result Deep analogy between thermodynamic and economic parameters established.
Financial market risk is modeled using thermodynamics, linking economic quantities to thermodynamic variables.
problem Model risk in financial markets caused by external information sources.
method Established a thermodynamic analogy between financial market variables and economic quantities, linking financial risk to entropy changes.
result Derivation of worst-case risk characterization rules using thermodynamics, leading to equilibrium and non-equilibrium thermodynamic evaluations.
New framework reveals thermodynamic principles for LLM training.
problem Understanding the training dynamics of large language models.
method Introducing Neural Thermodynamic Laws (NTL) under river-valley loss landscape assumptions.
result Key thermodynamic quantities and principles naturally emerge in LLM training.
This study compares different thermodynamic structure-informed neural networks for solving differential equations.
problem Improving the accuracy and physical consistency of neural network solutions to differential equations.
method Comprehensive evaluation of various thermodynamic formulations in physics-informed neural networks.
result Newtonian-residual-based PINNs fail to reliably recover physical quantities, while structure-preserving formulations enhance accuracy and robustness.
Geometric approach to quantum thermodynamics models state spaces and processes.
problem Quantum thermodynamics in the regime of non-equilibrium states.
method Contact geometry and principal fiber bundles to model quantum state spaces and processes.
result Geometric formulation reveals the fundamental thermodynamic relations and unattainability of the third law.
Develops a thermodynamics analogy for machine learning objectives.
problem Understanding machine learning objectives.
method Formal correspondence with thermodynamics.
result Implications for machine learning theory.
Aggregation of financial marketsq-fin.MF
We formalize how markets aggregate via arbitrage and quantify liquidity loss.
problem How financial markets aggregate and the loss of liquidity.
method Characterize markets via utility functions, use thermodynamics analogy, derive limit order book representation, compute aggregation loss.
result Arbitrage-mediated aggregation leads to market-dynamical entropy quantifying liquidity loss.
Validation of the Replica Trick for Simple Modelscond-mat.dis-nn
Validates replica trick for simple models using replica analytic continuation.
problem Validating replica trick for complex systems.
method Applying replica analysis to simple models, focusing on replica analytic continuation.
result Replica analytic continuation is a robust procedure in replica analysis.
New limits found for power, speed, and precision in physical communication.
problem Limits on power, speed, and precision in physical communication.
method Derivation involving a novel connection between friction and information geometry.
result Product of precision and speed is universally bounded by power dissipation.
Study analyzes Bayesian inference algorithms using dynamical functional approach.
problem Analysis of approximate inference algorithms for large Gaussian latent variable models.
method Dynamical functional approach to model nontrivial dependencies and obtain exact effective stochastic process.
result Closed-form expressions for the rate of convergence are derived and validated.
The Thermodynamic Costs of Simple Linear Regressioncond-mat.stat-mech
Study on thermodynamic costs of simple linear regression.
problem Understanding thermodynamic costs in machine learning models.
method Approximated thermodynamic lower bounds for exact and stochastic linear regression.
result Derived scaling laws for optimal dataset size based on generalization error.
New limits found for training deep learning models efficiently.
problem Optimizing the training speed of deep learning models without sacrificing accuracy.
method Applied stochastic thermodynamics to set speed limits for neural network training.
result Training neural networks is optimal within certain scaling assumptions.
Speed-accuracy relations for diffusion models: Wisdom from nonequilibrium thermodynamics and optimal transportcond-mat.stat-mech
Diffusion models' speed-accuracy relations derived from thermodynamics.
problem Understanding the trade-off between model speed and accuracy.
method Connecting diffusion models to thermodynamics and optimal transport.
result Speed-accuracy relations derived, providing insights into optimal learning protocols.
Geometric structures help in understanding thermodynamics.
problem Understanding thermodynamic systems using geometric methods.
method Using almost cosymplectic structures and variational arguments.
result Evolution equations are derived and discussed.
Geometric structures help in understanding nonequilibrium thermodynamics.
problem Formulating nonequilibrium thermodynamics using geometric objects.
method Using Dirac structures to formulate nonequilibrium thermodynamics.
result Dirac structures provide a consistent extension of mechanics to nonequilibrium thermodynamics.
BMTI method estimates densities without bins, outperforming traditional estimators.
problem Nonparametric, robust, and data-efficient density estimation in high-dimensional spaces.
method BMTI integrates log-density differences between neighboring points, weighted by uncertainties, using a maximum-likelihood formulation.
result BMTI reconstructs smooth profiles in high-dimensional spaces, outperforming traditional estimators.
Population Annealing as a Discrete-Time Schrödinger Bridgecond-mat.stat-mech
PA reinterpreted as SB problem, unifying thermodynamics and optimal transport.
problem Optimizing discrete-time paths between probability distributions.
method Schrödinger Bridge theory, solving variational problems.
result PA's reweighting step derived from Schrödinger system.
A new method uses heat diffusion to efficiently solve combinatorial optimization problems.
problem Challenges in combinatorial optimization due to discrete nature and limited search scope.
method Transforming the target function through heat diffusion to enable information flow and more efficient navigation.
result Superior performance across various combinatorial optimization problems.
Geometric study of thermodynamics using cotangent bundles.
problem Formulating classical thermodynamics using geometric structures.
method Contact geometry on cotangent bundles, homogeneous coordinates for intensive variables.
result Geometric formulation of thermodynamics with homogeneity properties.
Machine learning generates coarse-grained force fields for molecular dynamics.
problem Creating thermodynamically consistent coarse-grained models for larger systems.
method Hybrid architecture using graph neural networks to learn molecular features.
result Framework reproduces thermodynamics for small biomolecular systems.
Financial market dynamics compared to thermodynamics.
problem Understanding the dynamics of financial markets through thermodynamic principles.
method Analogy with Szilárd information engine to derive market temperature and information extraction.
result Informed traders' gains are bounded by market temperature and information.
Paper models stock price formation using Gibbs Grand-Canonical Ensemble.
problem Modeling stock price formation and temperature differences.
method Using Gibbs Grand-Canonical Ensemble, defining temperatures for bid and ask orders.
result Temperature difference between bid and ask orders correlates with VAO indicator.
Study thermodynamic framework for Monge-Ampère equations on real tori.
problem Monge-Ampère equations on real tori.
method Thermodynamic framework, point processes, convergence in law.
result Convergence in law of point processes associated with Monge-Ampère equations.
Modeling urban transformations, researchers identify phase transitions and thermodynamic efficiency.
problem Urban transformations and their effects on social interactions, transport, and income distribution.
method Developed a statistical-mechanical model of urban transformations, considering fast and slow dynamics.
result Identified phase transitions between dispersed and polycentric urban phases, quantified thermodynamic cost of transformation.
The paper explores how organisms and machines learn from their environment using machine learning, information theory, and thermodynamics.
problem How organisms and machines adapt to their environment.
method Machine learning, information theory, and thermodynamics approaches to understanding and modeling adaptation.
result Unified view of adaptation in organisms and machines based on principles of machine learning, information theory, and thermodynamics.
The author proposes a finance trading strategy named Entropy Oriented Trading and apply thermodynamics on the strategy. The state variables are chosen so that the strategy satisfies the second law of thermodynamics. Using the law, the author proves that the rate of investment (ROI) of the strategy is equal to or more t…
The paper connects symplectic geometry tools to statistical mechanics and thermodynamics.
problem Exploring connections between symplectic geometry and statistical mechanics/thermodynamics.
method Presentation of tools from symplectic and Poisson geometry, discussion of Lagrangian and Hamiltonian formalisms, explanation of manifold of motions, and application to statistical mechanics and thermodynamics.
result Generalization of thermodynamic equilibrium concepts using Lie groups.
Uncertainty relations and fluctuation theorems for Bayes netscond-mat.stat-mech
Derives fluctuation theorems and thermodynamic uncertainty relations for systems modeled as Bayes nets.
problem Entropy production in interacting systems modeled as Bayes nets.
method Derives fluctuation theorems and thermodynamic uncertainty relations for arbitrary sets and conditioned sets of systems in Bayes nets.
result Relates the entropy production of the overall system to the precisions of probability currents in individual systems.
The paper applies thermodynamics to financial markets to prove no-arbitrage constraints.
problem No arbitrage in financial markets under price impact.
method Stochastic thermodynamics applied to financial trading cycles.
result Proves any round-trip trading strategy yields non-positive expected profit.
New Holder bounds improve variational inference by flattening thermodynamic curves.
problem Improving variational inference by addressing performance gaps between theory and practice.
method Generalizing thermodynamic integration to weighted Holder mean, introducing Holder bounds.
result Holder bounds promise a one-step approximation of exact marginal log-likelihood.
The thermodynamics of portfolioscond-mat.stat-mech
We propose a new method of valuation of portfolios and their respective investing strategies. To this end we define a canonical ensemble of portfolios that allows to use the formalism thermodynamics.
Upper bound derived for informed traders' gains in a model, akin to thermodynamics.
problem Informed traders' gains in a financial model with finite horizon.
method Bayesian inference and entropic inequality.
result Upper bound for expected gain, analogous to thermodynamics.
Essay combines thermodynamics, economics, and geobiodynamics to model complex systems.
problem Understanding complex evolutionary systems in economics and geobiodynamics.
method Defines Roegenian Economy, links thermodynamics and economics, analyzes phase equilibrium, and discusses economic black holes.
result Roegenian economic systems are described as Carnot groups with phase equilibrium analysis.
Stochastic Geometric Mechanics in Nonequilibrium Thermodynamics: Schrödinger meets Onsagercond-mat.stat-mech
Schrödinger and Onsager's ideas linked in nonequilibrium thermodynamics.
problem Linking Schrödinger's variational problem with Onsager's nonequilibrium statistical mechanics.
method Analyzing the historical context and comparing the two approaches.
result Schrödinger's ideas have not yet been fully integrated into the classical context of Onsager's work.
On the role of geometry in statistical mechanics and thermodynamics I: Geometric perspectivecond-mat.stat-mech
The paper provides a geometric framework for understanding non-equilibrium thermodynamics.
problem Unclear geometric structure of GENERIC in non-equilibrium thermodynamics.
method Cotangent lifts of dynamics, splitting into holonomic and vertical representatives, and formulation within contact geometry.
result Physical meaning and explicit formulation of the second law of thermodynamics within evolution equations.
Thermodynamic Machine Learning through Maximum Work Productioncond-mat.stat-mech
Work maximization guides machine learning models in adaptive systems.
problem How machine learning models can be optimized for thermodynamic efficiency.
method Introducing thermodynamic principle to compare with maximum-likelihood principle.
result Maximum-work models are equivalent to maximum-likelihood models in adaptive systems.
This work develops a machine learning approach to EOS models that accounts for thermodynamic constraints and model uncertainty.
problem Developing accurate equation of state models for high energy-density experiments with inherent uncertainties.
method Physics-informed Gaussian process regression (GPR) framework to capture model uncertainty and thermodynamic constraints.
result The proposed framework reduces prediction uncertainty by incorporating thermodynamic constraints, as demonstrated for diamond carbon EOS.
Develops neural networks for learning physics of complex systems by enforcing thermodynamics principles.
problem Learning physics of complex systems from incomplete experimental data.
method Integrates port-metriplectic formalism with neural networks to enforce thermodynamics principles.
result Neural networks can learn physics of complex systems by parts, reducing learning burden.
We study the indefinite metric in the contact phase space of a homogeneous thermodynamical system introduced by R. Mrugala. We calculate the curvature tensor, Killing vector fields, second fundamental form of Legendre submanifolds of - constitutive surfaces of different homogeneous thermodynamical syste…