Publications de Matthieu Wyart

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In allosteric proteins, binding a ligand can affect function at a distant location, for example, by changing the binding affinity of a substrate at the active site. The induced fit and population shift models, which differ by the assumed number of stable c ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Which phenomenon slows down the dynamics in supercooled liquids and turns them into glasses is a long-standing question of condensed matter. Most popular theories posit that as the temperature decreases, many events must occur in a coordinated fashion on a ...

The jamming transition of non-spherical particles is fundamentally different from the spherical case. Non-spherical particles are hypostatic at their jamming points, while isostaticity is ensured in the case of the jamming of spherical particles. This stru ...

The macroscopic friction of particulate materials often weakens as the flow rate is increased, leading to potentially disastrous intermittent phenomena including earthquakes and landslides. We theoretically and numerically study this phenomenon in simple g ...

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

Hysteresis is a major feature of the solid-liquid transition in granular materials. This property, by allowing metastable states, can potentially yield catastrophic phenomena such as earthquakes or aerial landslides. The origin of hysteresis in granular fl ...

We introduce a numerical scheme to evolve functional elastic materials that can accomplish a specified mechanical task. In this scheme, the number of solutions, their spatial architectures, and the correlations among them can be computed. As an example, we ...

Amorphous solids tend to present an abundance of soft elastic modes, which diminish their transport properties, generate heterogeneities in their elastic response, and affect non-linear processes like thermal activation of plasticity. This is especially tr ...

Amorphous packings of nonspherical particles such as ellipsoids and spherocylinders are known to be hypostatic: The number of mechanical contacts between particles is smaller than the number of degrees of freedom, thus violating Maxwell's mechanical stabil ...

The plastic flow of amorphous materials displays non-local effects, characterized by a cooperativity length scale xi. We argue that these effects enter in the more general description of surface phenomena near critical points. Using this approach, we obtai ...

Failure of amorphous solids is fundamental to various phenomena, including landslides and earthquakes. Recent experiments indicate that highly plastic regions form elongated structures that are especially apparent near the maximal shear stress Sigma(max) w ...

Amorphous materials exhibit complex material properties with strongly nonlinear behaviors. Below a yield stress they behave as plastic solids, while they start to yield above a critical stress sigma(c). A key quantity controlling plasticity which is, howev ...

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 ...

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity of the loss landscape and of the dynamics within it, and ( ...

The low-temperature properties of amorphous solids are widely believed to be controlled by the low-frequency quasilocalized modes. However, what governs their spatial structure and density is unclear. We study these questions numerically in very large syst ...

Erosion shapes our landscape and occurs when a sufficient shear stress is exerted by a fluid on a sedimented layer. What controls erosion at a microscopic level remains debated, especially near the threshold forcing where it stops. Here we study, experimen ...

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this qu ...

We provide a theoretical description of dynamical heterogeneities in glass-forming liquids, based on the premise that relaxation occurs via local rearrangements coupled by elasticity. In our framework, the growth of the dynamical correlation length e and o ...

Matthieu Wyart

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Mechanics of Allostery: Contrasting the Induced Fit and Population Shift Scenarios

Learning sparse features can lead to overfitting in neural networks

Local vs. cooperative: Unraveling glass transition mechanisms with SEER

Infinitesimal asphericity changes the universality of the jamming transition

Friction law and hysteresis in granular materials

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Interparticle Friction Leads to Nonmonotonic Flow Curves and Hysteresis in Viscous Suspensions

Architecture and coevolution of allosteric materials

On variational arguments for vibrational modes near jamming

Universality of jamming of nonspherical particles

Scaling description of non-local rheology

Criticality in the Approach to Failure in Amorphous Solids

Inferring the flow properties of epithelial tissues from their geometry

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

How deep convolutional neural networks lose spatial information with training

Comparing dynamics: deep neural networks versus glassy systems

Spatial structure of quasilocalized vibrations in nearly jammed amorphous solids

Scale-free channeling patterns near the onset of erosion of sheared granular beds

Relative stability toward diffeomorphisms indicates performance in deep nets

Scaling Description of Dynamical Heterogeneity and Avalanches of Relaxation in Glass-Forming Liquids