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Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downstream tasks. However, traditional foundation models struggle…

Machine Learning · Computer Science 2025-04-24 Majid Farhadloo , Arun Sharma , Mingzhou Yang , Bharat Jayaprakash , William Northrop , Shashi Shekhar

Experimental limits on supersymmetry and similar theories are difficult to set because of the enormous available parameter space and difficult to generalize because of the complexity of single points. Therefore, more phenomenological,…

High Energy Physics - Experiment · Physics 2012-02-21 C. Gütschow , Z. Marshall

Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e.g., soft robotics. Due to the properties of flexible materials, the movements of soft…

Machine Learning · Computer Science 2024-06-18 Kaiyuan Tan , Peilun Li , Thomas Beckers

Many applications of contemporary science involve multiscale dynamics, which are typically characterized by the time and space scale separation of patterns of motion, with fewer slowly evolving variables and much larger set of faster…

Dynamical Systems · Mathematics 2016-04-08 Rafail V. Abramov

We review a collection of models of random simplicial complexes together with some of the most exciting phenomena related to them. We do not attempt to cover all existing models, but try to focus on those for which many important results…

Probability · Mathematics 2022-05-04 Omer Bobrowski , Dmitri Krioukov

From the mesoscopic point of view, a new concept of soft matching for mass points is proposed. Then a soft Lasso's approach to learn the soft dynamical equation for the physical mechanical relationship is proposed, too. Furthermore, a…

Fluid Dynamics · Physics 2023-11-14 Zongmin Wu , Ran Yang

A categorical framework for modeling and analyzing systems in a broad sense is proposed. These systems should be thought of as `machines' with inputs and outputs, carrying some sort of signal that occurs through some notion of time. Special…

Category Theory · Mathematics 2019-03-18 Patrick Schultz , David I. Spivak , Christina Vasilakopoulou

Using a time series model to mimic an observed time series has a long history. However, with regard to this objective, conventional estimation methods for discrete-time dynamical models are frequently found to be wanting. In fact, they are…

Statistics Theory · Mathematics 2015-03-19 Yingcun Xia , Howell Tong

We derive a mathematical model for the motion of several insulating rigid bodies through an electrically conducting fluid. Starting from a universal model describing this phenomenon in generality, we elaborate (simplifying) physical…

Mathematical Physics · Physics 2024-02-13 Jan Scherz , Anja Schlömerkemper

An irreversible thermodynamical theory of solids is presented where the kinematic quantities are defined in an automatically objective way. Namely, auxiliary elements like reference frame, reference time and reference configuration are…

Classical Physics · Physics 2015-12-22 Tamás Fülöp

The classical Euler--Poinsot case of the rigid body dynamics admits a class of simple but non-trivial integrable generalizations, which modify the Poisson equations describing the motion of the body in space. These generalizations possess…

Exactly Solvable and Integrable Systems · Physics 2015-06-15 Yuri N. Fedorov , Andrzej J. Maciejewski , Maria Przybylska

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We…

Machine Learning · Computer Science 2020-07-07 Yi-Ling Qiao , Junbang Liang , Vladlen Koltun , Ming C. Lin

In this paper we present a discussion of the basic aspects of the well-known problem of prediction and inference in physics, with specific attention to the role of models, the use of data and the application of recent developments in…

General Physics · Physics 2024-10-07 Luca Gammaitoni , Angelo Vulpiani

Skeletal muscles are living tissues that can undergo large deformations in short periods of time and that can be activated to produce force. In this paper we use the principles of continuum mechanics to propose a dynamic, fully non-linear,…

This work investigates twinlike scalar field models that support kinks with the same energy density and stability. We find the first order equations compatible with the equations of motion. We use them to calculate the conditions under…

High Energy Physics - Theory · Physics 2020-09-04 D. Bazeia , L. Losano , M. A. Marques , R. Menezes

A covariant hamiltonian formalism for the dynamics of compact spinning bodies in curved space-time in the test-particle limit is described. The construction allows a large class of hamiltonians accounting for specific properties and…

General Relativity and Quantum Cosmology · Physics 2016-12-21 J. W. van Holten

Dynamical system theory is a widely used technique in the analysis of cosmological models. Within this framework, the equations describing the dynamics of a model are recast in terms of dimensionless variables, which evolve according to a…

General Relativity and Quantum Cosmology · Physics 2023-08-16 Santiago García-Serna , J. Bayron Orjuela-Quintana , César A. Valenzuela-Toledo , Hernán Ocampo-Durán

A general framework for performing event-driven simulations of systems with semi-flexible or rigid bodies interacting under impulsive torques and forces is outlined. Two different approaches are presented. In the first, the dynamics and…

Statistical Mechanics · Physics 2007-05-23 Lisandro Hernandez de la Pena , Ramses van Zon , Jeremy Schofield , Sheldon B. Opps

We discuss the role coarse-grained models play in the investigation of the structure and thermodynamics of bilayer membranes, and we place them in the context of alternative approaches. Because they reduce the degrees of freedom and employ…

Soft Condensed Matter · Physics 2009-11-11 Marcus Mueller , Kirill Katsov , Michael Schick

We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge…

Artificial Intelligence · Computer Science 2018-01-10 Emmanuel de Bezenac , Arthur Pajot , Patrick Gallinari