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In this paper we consider a mathematical model which describes the equilibrium of two elastic rods attached to a nonlinear spring. We derive the variational formulation of the model which is in the form of an elliptic quasivariational…

Numerical Analysis · Mathematics 2023-09-11 Anna Ochal , Wiktor Prządka , Mircea Sofonea , Domingo A. Tarzia

This paper examines a proposal for gauging non-linear sigma models with respect to a Lie algebroid action. The general conditions for gauging a non-linear sigma model with a set of involutive vector fields are given. We show that it is…

Differential Geometry · Mathematics 2019-08-22 Kyle Wright

Lagrangian multiform theory is a variational framework for integrable systems. In this article we introduce a new formulation which is based on symplectic geometry and which treats position, momentum and time coordinates of a…

Mathematical Physics · Physics 2025-04-01 Vincent Caudrelier , Derek Harland

We consider the deformation of a geological structure with non-intersecting faults that can be represented by a layered system of viscoelastic bodies satisfying rate- and state-depending friction conditions along the common interfaces. We…

Numerical Analysis · Mathematics 2022-01-17 Carsten Gräser , Ralf Kornhuber , Joscha Podlesny

A Lie system is a non-autonomous system of ordinary differential equations describing the integral curves of a $t$-dependent vector field taking values in a finite-dimensional Lie algebra of vector fields. Lie systems have been generalised…

Mathematical Physics · Physics 2023-04-25 J. F. Cariñena , J. de Lucas , C. Sardón

This paper analyzes several different biases that emerge from the (possibly) low-precision nonparametric ingredient in a semiparametric model. We show that both the variance part and the bias part of the nonparametric ingredient can lead to…

Statistics Theory · Mathematics 2020-10-15 Jungjun Choi , Xiye Yang

We give an example of $C^k$-integrable almost complex structure that does not admit a corresponding $C^{k+1}$-complex coordinate system.

Complex Variables · Mathematics 2021-05-25 Liding Yao

We construct several variational integrators--integrators based on a discrete variational principle--for systems with Lagrangians of the form L = L_A + epsilon L_B, with epsilon << 1, where L_A describes an integrable system. These…

Astrophysics · Physics 2009-01-25 Will M. Farr

In this note, first the Recurrent Variational Approach (RVA) is introduced by using as example a non-trivial spin-model, the spin-1/2 antiferromagnetic two-leg-ladder. Then, a first application of this scheme to the electronic structure of…

Condensed Matter · Physics 2007-05-23 S. Pleutin , E. Jeckelmann , M. A. Martin-Delgado , G. Sierra

We consider the nonparametric robust estimation problem for regression models in continuous time with semi-Markov noises. An adaptive model selection procedure is proposed. Under general moment conditions on the noise distribution a sharp…

Statistics Theory · Mathematics 2017-03-28 Vlad Barbu , Slim Beltaif , Serguei Pergamenchtchikov

A variational integrator of arbitrarily high-order on the special orthogonal group $SO(n)$ is constructed using the polar decomposition and the constrained Galerkin method. It has the advantage of avoiding the second-order derivative of the…

Numerical Analysis · Mathematics 2022-01-27 Xuefeng Shen , Khoa Tran , Melvin Leok

Open quantum systems host a wide range of intriguing phenomena, yet their simulation on well-controlled quantum devices is challenging, owing to the exponential growth of the Hilbert space and the inherently non-unitary nature of the…

We introduce a unified formulation of variational methods for simulating ground state properties of quantum many-body systems. The key feature is a novel variational method over quantum circuits via infinitesimal unitary transformations,…

Quantum Physics · Physics 2009-11-13 Christopher M. Dawson , Jens Eisert , Tobias J. Osborne

Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt…

Methodology · Statistics 2024-02-29 Lujia Bai , Weichi Wu

Many-body localization is shown to suppress imaginary parts of complex eigenenergies for general non-Hermitian Hamiltonians having time-reversal symmetry. We demonstrate that a real-complex transition, which we conjecture occurs upon…

Disordered Systems and Neural Networks · Physics 2019-09-04 Ryusuke Hamazaki , Kohei Kawabata , Masahito Ueda

Variational symplectic algorithms have recently been developed for carrying out long-time simulation of charged particles in magnetic fields. As a direct consequence of their derivation from a discrete variational principle, these…

Plasma Physics · Physics 2015-06-18 Jonathan Squire , Hong Qin , William M. Tang

A common assumption in machine learning is that samples are independently and identically distributed (i.i.d). However, the contributions of different samples are not identical in training. Some samples are difficult to learn and some…

Machine Learning · Computer Science 2021-11-23 Ou Wu , Weiyao Zhu , Yingjun Deng , Haixiang Zhang , Qinghu Hou

A semi-parametric, non-linear regression model in the presence of latent variables is introduced. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex networked system. This new formulation allows…

Machine Learning · Statistics 2018-06-29 Jonathan Mei , José M. F. Moura

We propose the difference discrete variational principle in discrete mechanics and symplectic algorithm with variable step-length of time in finite duration based upon a noncommutative differential calculus established in this paper. This…

Mathematical Physics · Physics 2018-01-17 Xu-Dong Luo , Han-Ying Guo , Yu-Qi Li , Ke Wu

Complex systems often contain feedback loops that can be described as cyclic causal models. Intervening in such systems may lead to counterintuitive effects, which cannot be inferred directly from the graph structure. After establishing a…

Machine Learning · Computer Science 2022-12-15 Michel Besserve , Bernhard Schölkopf