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arXiv research

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.

169,051 papers · 148 categories

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51102152203 · Jun 202019922001200920182026
48 results for physical invariances

Generative models synthesize microstructures respecting physical invariances.

problem Synthesizing microstructures given limited images and physical constraints.
method Three generative models: WGAN, physics-informed GAN, and hybrid model.
result Synthesized microstructures respect physical invariances and latent variable constraints.

INO learns physical models with momentum conservation laws.

problem Learning physical models without preserving fundamental laws.
method Designing an invariant neural operator that automatically satisfies momentum conservation laws.
result The model learns complex material behaviors and achieves state-of-the-art accuracy and efficiency.

Physics-informed neural networks improve by measuring effective dimensionality of constraints.

problem Task interference in physics-informed neural networks due to shared parameter space.
method Introduce effective dimensionality (deffd_{eff}) as an operator invariant to quantify constraints.
result Effective dimensionality measures unconstrained parameter directions, independent of network architecture.

A new method uses physics-informed neural networks to solve reliability analysis problems without simulations.

problem Solving reliability analysis problems without the need for expensive simulations.
method Physics-informed neural networks to learn directly from problem physics.
result Eliminates the need for expensive simulations and achieves highly accurate results.

Polynomial invariants classify molecular chains based on their contact arrangements.

problem No established invariants for molecular chains with both hard and soft contacts.
method Developed polynomial invariants for circuit topology of molecular chains.
result Polynomial invariants efficiently classify chains with various contact types.

IsoGCNs learn invariant and equivariant graph features for efficient simulations.

problem Learning isometric transformation invariant and equivariant features in graphs for simulations.
method Transformation invariant and equivariant Graph Convolutional Networks (IsoGCNs).
result IsoGCNs outperform state-of-the-art methods on geometrical and physical simulation tasks.

MetaPhysiCa tackles robust physics-informed machine learning for OOD tasks.

problem Designing robust PIML methods for OOD forecasting tasks in physics.
method Meta-learning procedure for causal structure discovery including invariant risk minimization.
result Significantly outperforms existing PIML and deep learning methods in OOD tasks.

The goal of this article is twofold. First, we find a natural home for the double affine Hecke algebras (DAHA) in the physics of BPS states. Second, we introduce new invariants of torus knots and links called "hyperpolynomials" that address the "problem of negative coefficients" often encountered in DAHA-based approach…

2015-05-07abs ↗pdf ↗

In their papers published in 1993 and 1994, by expressing certain physical quantity in two distinct ways, Bershadsky-Cecotti-Ooguri-Vafa discovered a remarkable equivalence between Ray-Singer analytic torsion and elliptic instanton numbers for Calabi-Yau threefolds. After their discovery, in a paper published in 2008, …

2014-08-30abs ↗pdf ↗

Equivariant flows generate symmetric distributions for complex systems.

problem Generating symmetric distributions for complex systems with exact likelihood.
method Equivariant normalizing flows that preserve symmetries.
result Equivariant flows generate symmetric distributions that are invariant to symmetries in physical systems.

Steerable E(3) Graph Neural Networks incorporate geometric and physical covariant information.

problem Incorporating covariant information like position, force, velocity, or spin in graph neural networks.
method Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that use steerable MLPs to incorporate geometric and physical covariant information.
result SEGNNs improve upon classic linear point convolutions and recent equivariant graph networks that send invariant messages.

3D models vulnerable to adversarial attacks, new method improves success rate and naturalness.

problem Vulnerability of 3D deep learning models to adversarial examples in the physical world.
method ε-isometric (εε-ISO) attack considering geometric properties and invariance to physical transformations.
result Significantly improved attack success rate and naturalness of 3D adversarial examples.

Paper introduces a method to generate physically feasible dynamics with physical priors.

problem Challenges in generating physically feasible dynamics under physical priors.
method Seamlessly incorporates physical priors into diffusion-based generative models.
result Efficient generation of physically realistic dynamics across various physical phenomena.

We prove the ADO invariants are a q-holonomic family and establish recursion relations.

problem Understanding the qq-holonomic properties of ADO link invariants.
method Proving the ADO invariants are a qq-holonomic family and establishing recursion relations.
result The ADO invariants for r2r\geq 2 are a qq-holonomic family, satisfying independent recursion relations.

The paper develops GPR models for hyperelastic materials, improving accuracy and rotational invariance.

problem Modeling stress tensors of hyperelastic materials with fewer training examples and higher accuracy.
method Developed three approaches: direct stress tensor modeling, embedding rotational invariance, and recovering strain-energy density.
result Improved GPR models achieve higher accuracy and rotational invariance with fewer training examples.

Gopakumar-Vafa large N duality is a correspondence between Chern-Simons invariants of a link in a 3-manifold and relative Gromov-Witten invariants of a 6-dimensional symplectic manifold relative to a Lagrangian submanifold. We address the correspondence between the Chern-Simons free energy of S^3 with no link and the G…

2007-01-20abs ↗pdf ↗

New method identifies physical constants from video data alone.

problem Identifying physical constants from video data.
method Proves level-set slope-coverage condition ensures local affine mapping to true physical state, enabling exact parameter recovery.
result Underdamped systems identifiable from a single video clip, other regimes require three diverse trajectories.

Deser and Nepomechie established a relationship between masslessness and rigid conformal invariance by coupling to a background metric and demanding local Weyl invariance, a method which applies neither to massive theories nor theories which rely upon gauge invariances for masslessness. We extend this method to describ…

2008-10-16abs ↗pdf ↗

We provide a physical definition of new homological invariants Ha(M3)\mathcal{H}_a (M_3) of 3-manifolds (possibly, with knots) labeled by abelian flat connections. The physical system in question involves a 6d fivebrane theory on M3M_3 times a 2-disk, D2D^2, whose Hilbert space of BPS states plays the role of a basic build…

2017-01-23abs ↗pdf ↗

Revisits Vafa-Witten theory, deriving new invariants and homologies.

problem Understanding Vafa-Witten equations and their invariants.
method Physical derivation and mathematical analysis of Vafa-Witten equations.
result Novel invariants and Floer homologies derived from Vafa-Witten equations.

Constructs 3D topological field theories from a specific quantum group, linking to physics invariants.

problem Developing topological field theories from non-semisimple quantum groups.
method Using the unrolled quantum group of osp(12)\mathfrak{osp}(1 \vert 2) and a relative modular structure on weight modules.
result Establishes a connection between constructed invariants and physicists' Z^\widehat{Z}-invariants.

The paper studies minimal resistance dynamics in radial fields, finding unique solutions for incompressible flows.

problem Nonlinear dynamics of minimal resistance in radial fields.
method Analysis of two non-equilibrium scenarios: scale-invariant free expansion and incompressible source flow.
result Incompressible flow acts as a structural regularizer, admitting unique, smooth, and strictly concave solutions.

One considers a special class of PDEs systems and one determines the associated symmetry group. Particulary, for the Blair system, one finds the symmetry group. A solutions of the Blair system gives a conformally flat contact metric structure and also it defines a "force-free" model of solar physics. By using the symme…

1999-10-26abs ↗pdf ↗

Defines observer-invariant time derivatives on moving surfaces.

problem Deriving appropriate definitions for time derivatives on surfaces that move.
method Systematically derived from spacetime settings, considering observer-invariance and covariance principles.
result Formulations applicable for computations of tangential n-tensor fields on moving surfaces.