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.
Paper connects RL and non-equilibrium statistical mechanics for entropy-regularized RL.
The paper provides a geometric framework for understanding non-equilibrium thermodynamics.
In this paper I give a brief introduction to a family of simple but non-trivial models designed to increase our understanding of collective processes in markets, the so-called Minority Games, and their non-equilibrium statistical mathematical analysis. Since the most commonly studied members of this family define disor…
We analyze the statistics of daily price change of stock market in the framework of a statistical physics model for the collective fluctuation of stock portfolio. In this model the time series of price changes are coded into the sequences of up and down spins, and the Hamiltonian of the system is expressed by spin-spin…
Rate GENERIC extends thermodynamics principles to non-equilibrium systems.
A new method relaxes molecules without needing non-equilibrium data.
Framework preserves emergent physics in non-equilibrium systems from particle trajectories.
Generative diffusion models exhibit phase transitions in statistical mechanics, impacting their performance.
SNF combines stochastic and deterministic steps to sample complex distributions.
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
New CGMD model predicts non-equilibrium processes better than existing methods.
Develops a method for non-equilibrium importance sampling to estimate expectations and constants.
This paper tackles traffic volume estimation challenges with a deep learning method.
Alternative finance models from physics for non-equilibrium systems.
Deep neural networks near edge of chaos show universal scaling laws.
Tractable model explains market dynamics using Langevin and SUSY QM.
Neural network models colloidal particle dynamics in non-equilibrium systems.
I present a unified discussion of several recently published results concerning the escalation, timing and severity of violent events in human conflicts and global terrorism, and set them in the wider context of real-world and cyber-based collective violence and illicit activity. I point out how the borders distinguish…
Mathematical model predicts international trade and global economy dynamics.
Investigates spontaneous symmetry breaking in non-equilibrium systems.
NEO combines orbits to sample and estimate complex distributions.
We seek to infer the parameters of an ergodic Markov process from samples taken independently from the steady state. Our focus is on non-equilibrium processes, where the steady state is not described by the Boltzmann measure, but is generally unknown and hard to compute, which prevents the application of established eq…
A new method uses deep learning to predict rare events in complex systems.
Investigates fluid flow perturbations using geometric theory.
New method reconstructs non-equilibrium stochastic systems from data.
Superstatistics is a widely employed tool of non-equilibrium statistical physics which plays an important role in analysis of hierarchical complex dynamical systems. Yet, its "canonical" formulation in terms of a single nuisance parameter is often too restrictive when applied to complex empirical data. Here we show tha…
Motivated by a probabilistic approach to Kahler-Einstein metrics we consider a general non-equilibrium statistical mechanics model in Euclidean space consisting of the stochastic gradient flow of a given (possibly singular) quasi-convex N-particle interaction energy. We show that a deterministic "macroscopic" evolution…
The origin of the long-range memory in the non-equilibrium systems is still an open problem as the phenomenon can be reproduced using models based on Markov processes. In these cases a notion of spurious memory is introduced. A good example of Markov processes with spurious memory is stochastic process driven by a non-…
We consider a simple stochastic model of a urban rental housing market, in which the interaction of tenants and landlords induces rent fluctuations. We simulate the model numerically and measure the equilibrium rent distribution, which is found to be close to a lognormal law. We also study the influence of the density …
This work uses statistical mechanics to explain AI learning.
Researchers use Gaussian processes to approximate Lagrange multipliers for Maximum-Entropy distributions.
We develop a formalism to study linearized perturbations around the equilibria of a pure exchange economy. With the use of mean field theory techniques, we derive equations for the flow of products in an economy driven by heterogeneous preferences and probabilistic interaction between agents. We are able to show that i…
We extend our studies of a quantum field model defined on a lattice having the dilation group as a local gauge symmetry. The model is relevant in the cross-disciplinary area of econophysics. A corresponding proposal by Ilinski aimed at gauge modeling in non-equilibrium pricing is realized as a numerical simulation of t…
The purpose of this work is to explore the role that random arbitrage opportunities play in pricing financial derivatives. We use a non-equilibrium model to set up a stochastic portfolio, and for the random arbitrage return, we choose a stationary ergodic random process rapidly varying in time. We exploit the fact that…
New theory predicts deep neural networks can operate in an extended critical regime without fine-tuning.
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and…
Formulates mechanics for probability distributions on statistical manifold.
Thermodynamic integration (TI) for computing marginal likelihoods is based on an inverse annealing path from the prior to the posterior distribution. In many cases, the resulting estimator suffers from high variability, which particularly stems from the prior regime. When comparing complex models with differences in a …
We propose a simple quantitative model of Schumpeterian economic dynamics. New goods and services are endogenously produced through combinations of existing goods. As soon as new goods enter the market they may compete against already existing goods, in other words new products can have destructive effects on existing …
The paper uses geometry to understand how neural networks learn.
The Yukawa term in statistical mechanics quantifies information generation.
A new mechanism for differentially private Fréchet mean on SPD matrices.
The relativistic quantum mechanic approach is used to develop a stock market dynamics. The relativistic is conceptional here as the meaning of big external volatility or volatility shock on a financial market. We used a differential geometry approach with the parallel transport of the prices to obtain a direct shift of…
This paper analyzes MFVBI for GMM using statistical mechanics.
I introduce a new geometrical approach to thermo--statistical mechanics. Here I highlight the main physical ideas, and how do they translate into geometrical language. I contrast the present approach with previous thermo--statistical--geometrical formalisms, (pseudo-)Riemannian [Weinhold 1975; Ruppeiner 1979] as well a…
During the last two years, Europe has been facing a debt crisis, and Greece has been at its center. In response to the crisis, drastic actions have been taken, including the halving of Greek debt. Policy makers acted because interest rates for sovereign debt increased dramatically. High interest rates imply that defaul…
Geometric approach to quantum thermodynamics models state spaces and processes.
New method uses quantum annealing and VAN for better statistical mechanics calculations.