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.
Discussing AI's difficulty and physics' simplicity, suggesting AI benefits from physics principles.
We explain the meaning of local symmetries in physics.
Kichenassamy's work spans theoretical physics, from relativity to applications.
The paper extends physics-based information maximization to complex bandit problems.
Develops neural networks for learning physics of complex systems by enforcing thermodynamics principles.
This work uses statistical mechanics to explain AI learning.
The article reviews how gradient flow systems on hypergraphs connect to information geometry and nonequilibrium physics.
New bandit algorithm maximizes information gain.
Model uses statistical physics principles to predict financial market volatility and returns.
We consider the concept of equilibrium in economic systems from statistical mechanics viewpoint. A new method is suggested for computing the premium on this basis. The Bühlmann economic premium principle is derived as a special case of our method.
AIF improves physical AI agents' performance in dynamic environments.
GeoHNN models physics laws for stable, accurate predictions.
This study compares different thermodynamic structure-informed neural networks for solving differential equations.
Unified taxonomy for ML uncertainty in physics, validated.
Most of the econometric and econophysics models have been borrowed from the statistical physics, and as a cosequence, a new interdisciplinary science called econophysics has emerged. In this paper we planned to extend the analogy between different economic processes or phenomena and processes and phenomena from differe…
Work maximization guides machine learning models in adaptive systems.
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatio…
This paper presents a continuous variable generalization of the Aoki-Yoshikawa sectoral productivity model. Information theoretical methods from the Frieden-Soffer extreme physical information statistical estimation methodology were used to construct exact solutions. Both approaches coincide in first order approximatio…
Turbo-Sim generates models from physics principles, improving interpretability and flexibility.
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
Paper reconciles two methods of describing Riemannian spaces.
Unified geometric principles unify neural network architectures.
New neural network enforces mass conservation for better ice flow predictions.
This article is concerned with learning and stochastic control in physical systems which contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parametrized covariance structures. The resulting latent force models (LFMs) can be seen as hybrid models that contain a first…
From positions, attained by modern theoretical physics in understanding of the universe bases, the methodological and philosophical analysis of fundamental physical concepts and their formal and informal connections with the real economic measurings is carried out. Procedures for heterogeneous economic time determinati…
Blockchain aims to improve trust in AI systems, but lacks systematic studies.
Physics-enhanced NNs improve predictive accuracy in small data scenarios.
High-precision machine learning reduces particle physics simulations by orders of magnitude.
Study maximal hypersurfaces in open spacetimes using a maximum principle.
In this paper will be applied some principles and methods from econophysics in the case of the direct foreign investitions (D.F.I.), particularised for the Greenfield type, and mixed firms of trade and industrial production (Joint Ventures). To this aim will be used some similarities and parallelisms between the mentio…
In this paper we form a general conservation law that unifies a class of physics field theories. For this we first introduce the notion of a general field as a formal sum differential forms on a Minkowski manifold. Thereafter, we employ the action principle to define the conservation law for such general fields. By con…
We investigate a statistical-static hedging technique for pricing assets considered as single-step stochastic cash flows. The valuation is based on constructing in a canonical way a European style derivative on a benchmark security such that the physical payoff distribution coincides with the (corrected) physical asset…
We analyze the relationships between game theory and quantum mechanics and the extensions to statistical physics and information theory. We use certain quantization relationships to assign quantum states to the strategies of a player. These quantum states are contained in a density operator which describes the new quan…
PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.
We use a Lagrangian perspective to show the limiting absorption principle on Riemannian scattering, i.e. asymptotically conic, spaces, and their generalizations. More precisely we show that, for non-zero spectral parameter, the `on spectrum', as well as the `off-spectrum', spectral family is Fredholm in function spaces…
Variationality of conformal geodesics fails in higher dimensions.
Extract symbolic models from deep learning with inductive biases.
General equilibrium equations in economics play the same role with many-body Newtonian equations in physics. Accordingly, each solution of the general equilibrium equations can be regarded as a possible microstate of the economic system. Since Arrow's Impossibility Theorem and Rawls' principle of social fairness will p…
Survey on advanced gauge theory concepts.
Single model learns physics from diverse data.
One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes …
In this note, we derive an approximation for the mean curvature normal vector on vertices of triangulated surface meshes from the Young-Laplace equation and the force balance principle. We then demonstrate that the approximation expression from our physics-based derivation is equivalent to the discrete Laplace-Beltrami…
The variational principle and the corresponding differential equation for geodesic circles in two dimensional (pseudo)-Riemannian space are being discovered. The relationship with the physical notion of uniformly accelerated relativistic particle is emphasized. The known form of spin-curvature interaction emerges due t…
In this paper we state the fundamental principles of the gauge approach to financial economics and demonstrate the ways of its application. In particular, modelling of realistic price processes is considered for an example of S&P500 market index. Derivative pricing and portfolio theory are also briefly discussed.
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating deterministic future predictions. Of most practical relevance are automated approaches to theory building that scale efficiently for complex sy…
Enhances neural operators with physics knowledge for more accurate simulations.
Framework augments physical models with deep learning for complex dynamics forecasting.
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model…