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Geometry of variational methods: dynamics of closed quantum systems [PDF]
SciPost Physics, 2020 We present a systematic geometric framework to study closed quantum systems based on suitably chosen variational families. For the purpose of (A) real time evolution, (B) excitation spectra, (C) spectral functions and (D) imaginary time evolution, we ...
Lucas Hackl, Tommaso Guaita, Tao Shi, Jutho Haegeman, Eugene Demler, J. Ignacio Cirac
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Variational Methods for Normal Integration [PDF]
Journal of Mathematical Imaging and Vision, 2017 The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry, etc. Inspired by edge-preserving methods from image processing, we study in this paper several variational approaches for normal integration, with a focus on non-rectangular
Yvain Quéau +2 more
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A review of operational methods of variational and ensemble‐variational data assimilation
Quarterly Journal of the Royal Meteorological Society, 2017 Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation.
R. Bannister
exaly +2 more sources
Computer Vision, A Reference Guide, 2014 This contribution presents derivative-based methods for local sensitivity analysis, called Variational Sensitivity Analysis (VSA). If one defines an output called the response function , its sensitivity to inputs variations around a nominal value can be ...
Hebert Montegranario, Jairo Espinosa
semanticscholar +3 more sources
The Variational Quantum Eigensolver: A review of methods and best practices [PDF]
Physics reports, 2021 The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian, a problem that is central to quantum chemistry and condensed matter physics.
J. Tilly +10 more
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Variational methods for simulation-based inference [PDF]
International Conference on Learning Representations, 2022 We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or likelihood-ratio-estimation) with variational inference to achieve a ...
Manuel Glöckler +2 more
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Variational methods for finding periodic orbits in the incompressible Navier–Stokes equations [PDF]
Journal of Fluid Mechanics, 2021 Unstable periodic orbits are believed to underpin the dynamics of turbulence, but by their nature are hard to find computationally. We present a family of methods to converge such unstable periodic orbits for the incompressible Navier–Stokes equations ...
J. Parker, T. Schneider
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Performance comparison of optimization methods on variational quantum algorithms [PDF]
Physical Review A, 2021 Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametrized
Xavier Bonet-Monroig +7 more
semanticscholar +1 more source
The variational method of moments
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023 Abstract The conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables, a prominent example being instrumental variable regression. We introduce a very general class of estimators called the variational method of moments (VMM), motivated by a variational minimax reformulation
Andrew Bennett, Nathan Kallus
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Network element methods for linear elasticity
Comptes Rendus. Mécanique, 2023 We explain how to derive a network element for the linear elasticity problem. After presenting sufficient conditions on the network for the validity of a discrete Korn inequality, we also propose several variations of the presented method and in ...
Coatléven, Julien
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