**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Person# Matthieu Wyart

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related units

Loading

Courses taught by this person

Loading

Related research domains

Loading

Related publications

Loading

People doing similar research

Loading

Related units (2)

Courses taught by this person (3)

PHYS-316: Statistical physics II

Introduction à la théorie des transitions de phase

PHYS-435: Statistical physics III

This course introduces statistical field theory, and uses concepts related to phase transitions to discuss a variety of complex systems (random walks and polymers, disordered systems, combinatorial optimisation, information theory and error correcting codes).

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.

Related research domains (76)

Amorphous solid

In condensed matter physics and materials science, an amorphous solid (or non-crystalline solid) is a solid that lacks the long-range order that is characteristic of a crystal. The terms "glass" and

Friction

Friction is the force resisting the relative motion of solid surfaces, fluid layers, and material elements sliding against each other. There are several types of friction:
*Dry friction is a force

Particle

In the physical sciences, a particle (or corpuscule in older texts) is a small localized object which can be described by several physical or chemical properties, such as volume, density, or mass. T

People doing similar research (63)

Related publications (92)

Loading

Loading

Loading

Thomas Willem Jan de Geus, Wencheng Ji, Marko Popovic, Matthieu Wyart

Amorphous solids such as coffee foam, toothpaste, or mayonnaise display a transient creep flow when a stress E is suddenly imposed. The associated strain rate is commonly found to decay in time as gamma_ -t-nu, followed either by arrest or by a sudden fluidization. Various empirical laws have been suggested for the creep exponent nu and fluidization time tau fin experimental and numerical studies. Here, we postulate that plastic flow is governed by the difference between E and the transient yield stress Et(gamma) that characterizes the stability of configurations visited by the system at strain gamma. Assuming the analyticity of Et(gamma) allows us to predict nu and asymptotic behaviors of tau f in terms of properties of stationary flows. We test successfully our predictions using elastoplastic models and published experimental results.

Thomas Willem Jan de Geus, Matthieu Wyart

Slip at a frictional interface occurs via intermittent events. Understanding how these events are nucleated, can propagate, or stop spontaneously remains a challenge, central to earthquake science and tribology. In the absence of disorder, rate-and-state approaches predict a diverging nucleation length at some stress a*, beyond which cracks can propagate. Here we argue for a flat interface that disorder is a relevant perturbation to this description. We justify why the distribution of slip contains two parts: a power law corresponding to "avalanches" and a "narrow" distribution of system-spanning "fracture" events. We derive novel scaling relations for avalanches, including a relation between the stress drop and the spatial extension of a slip event. We compute the cut-off length beyond which avalanches cannot be stopped by disorder, leading to a system-spanning fracture, and successfully test these predictions in a minimal model of frictional interfaces.

Antonio Sclocchi, Umberto Maria Tomasini, Matthieu Wyart

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the kernel, it fits well the coefficients associated with the O(P) largest eigenvalues, where P is the size of the training set. This prediction works very well on benchmark data sets such as images, yet the assumptions these approaches make on the data are never satisfied in practice. To clarify when the spectral bias prediction holds, we first focus on a one-dimensional model where rigorous results are obtained and then use scaling arguments to generalize and test our findings in higher dimensions. Our predictions include the classification case f(x) =sign(x(1)) with a data distribution that vanishes at the decision boundary p(x) similar to x(1)(chi). For chi > 0 and a Laplace kernel, we find that (i) there exists a cross-over ridge lambda(d,chi)*(P) similar to P-1/d+chi such that for lambda >> lambda(d,chi)*(P), the replica method applies, but not for lambda < lambda(d,chi)*(P), (ii) in the ridgeless case, spectral bias predicts the correct training curve exponent only in the limit d -> infinity.