High-order geometric integrators for the variational Gaussian approximation

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Hierarchy of quasisymmetries and degeneracies in the CoSi family of chiral crystal materials

Philip Johannes Walter Moll, Chunyu Guo, Yi-Chiang Sun

In materials, certain approximated symmetry operations can exist in a lower-order approximation of the effective model but are good enough to influence the physical responses of the system, and these approximated symmetries were recently dubbed "quasisymme ...

AMER PHYSICAL SOC2023

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Liang Xu

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

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Mean flow modeling in high-order nonlinear Schrodinger equations

Debbie Eeltink

The evaluation and consideration of the mean flow in wave evolution equations are necessary for the accurate prediction of fluid particle trajectories under wave groups, with relevant implications in several domains, from the transport of pollutants in the ...

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Possible restoration of particle-hole symmetry in the 5/2-quantized Hall state at small magnetic field

Frédéric Mila, Loïc Jean Pierre Herviou

Motivated by the experimental observation of a quantized 5/2 thermal conductance at filling nu = 5/2, a result incompatible with both the Pfaffian and the anti-Pfaffian states, we have pushed the expansion of the effective Hamiltonian of the 5/2-quantized ...

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Region Extraction in Mesh Intersection

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Region extraction is a very common task in both Computer Science and Engineering with several applications in object recognition and motion analysis, among others. Most of the literature focuses on regions delimited by straight lines, often in the special ...

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Learning ground states of gapped quantum Hamiltonians with Kernel Methods

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Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

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Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

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Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

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Efficient geometric integrators for the linear and nonlinear time-dependent Schrödinger equation

Julien Roulet

Many physical and chemical reactions are driven by nonadiabatic processes, which imply the breakdown of the celebrated Born-Oppenheimer approximation. To understand these processes, experimentalists employ spectroscopic techniques. However, the obtained re ...

EPFL2022

TimeEvolver: A program for time evolution with improved error bound

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We present TimeEvolver, a program for computing time evolution in a generic quantum system. It relies on well-known Krylov subspace techniques to tackle the problem of multiplying the exponential of a large sparse matrix iH, where His the Hamiltonian, with ...

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High-order geometric integrators for nonadiabatic molecular quantum dynamics and their applications to explore conical intersections

Seonghoon Choi

Accurate simulations of molecular quantum dynamics are crucial for understanding numerous natural processes and experimental results. Yet, such high-accuracy simulations are challenging even for relatively simple systems where the Born-Oppenheimer approxim ...

EPFL2022

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Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Wiley2023

High-order geometric integrators for nonadiabatic molecular quantum dynamics and their applications to explore conical intersections

Seonghoon Choi

EPFL2022

Efficient geometric integrators for the linear and nonlinear time-dependent Schrödinger equation

Julien Roulet

EPFL2022