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Publications associées (30)

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high ...

EPFL2022

Conformal Field Theory at the Lattice Level: Discrete Complex Analysis and Virasoro Structure

Clément Hongler

Critical statistical mechanics and Conformal Field Theory (CFT) are conjecturally connected since the seminal work of Beliavin et al. (Nucl Phys B 241(2):333-380, 1984). Both exhibit exactly solvable structures in two dimensions. A long-standing question ( ...

SPRINGER2022

Tensor network investigation of frustrated Ising models

Jeanne Colbois

In spin systems, geometrical frustration describes the impossibility of minimizing simultaneously all the interactions in a Hamiltonian, often giving rise to macroscopic ground-state degeneracies and emergent low-temperature physics. In this thesis, combin ...

EPFL2022

Molecular dynamics simulations of the evaporation of hydrated ions from aqueous solution

Philip Robin Loche

Although important for atmospheric processes and gas-phase catalysis, very little is known about the hydration state of ions in the vapor phase. Here we study the evaporation energetics and kinetics of a chloride ion from liquid water by molecular dynamics ...

NATURE PORTFOLIO2022

Tensor network investigation of the hard-square model

Frédéric Mila, Samuel Louis Nyckees

Using the corner-transfer matrix renormalization group to contract the tensor network that describes its partition function, we investigate the nature of the phase transitions of the hard-square model, one of the exactly solved models of statistical physic ...

AMER PHYSICAL SOC2022

Multilevel ensemble Kalman filtering for spatio-temporal processes

Fabio Nobile, Hakon Andreas Hoel

We design and analyse the performance of a multilevel ensemble Kalman filter method (MLEnKF) for filtering settings where the underlying state-space model is an infinite-dimensional spatio-temporal process. We consider underlying models that needs to be si ...

Springer2021

Masksembles for Uncertainty Estimation

Pascal Fua, Pierre Bruno Baqué, Nikita Durasov, Timur Bagautdinov

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expe ...

2021

A Statistical Physics approach to a multi-channel Wigner spiked model

Emanuele Mingione, Diego Alberici

In this letter we present a finite temperature approach to a high-dimensional inference problem, the Wigner spiked model, with group-dependent signal-to-noise ratios. For two classes of convex and non-convex network architectures the error in the reconstru ...

IOP Publishing - EPL Association2021

Architecture, environnement et artefact total : la ville selon Reyner Banham et Oswald Mathias Ungers

Thibaut Jean Bernard Pierron

When looking at the city and the land, current architectural practices face a double limitation. On the one hand, the discipline is asked to react to its own specter: the body of authoritarian spatial devices based on a questionable tradition of which the ...

EPFL2021

Uncertainty estimation for molecular dynamics and sampling

Michele Ceriotti, Venkat Kapil, Yongbin Zhuang, Kevin Rossi, Edgar Albert Engel, Federico Grasselli, Giulio Imbalzano

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...

2021

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