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Yohai Bar Sinai

Visitng Scientist

Google Research

Biography

I am physicist, currently a visiting scientist at Google Research, generally interested in the physics of complex non linear mechanical systems. I’m currently excited about applying Data-Driven techniques (“machine learning”) to study these systems. My current efforts concentrate on using machine learning for systematic coarse graining of PDEs, and on extracting and identifying structure from experimental data. In the past I studied the dynamics of frictional interfaces, some aspects of disordered systems (say, this and that), a bit of biomechanics, and stuff like that.

In 2020 I’ll be starting a research group in the Department of Condensed Matter Physics, at Tel Aviv University’s School of Physics and Astronomy. I’m looking for graduate students and postdocs to join me. For details, drop me a line. It will be fun.

Projects

Data driven physics

Learning physics with data-driven algorithms

Friction

Frictional motion is highly complex and non-linear, can be imaged directly, and is practically important. What more do you need?

Recent Publications

Geometric charges and nonlinear elasticity of soft metamaterials

Problems of flexible mechanical metamaterials, and highly deformable porous solids in general, are rich and complex due to nonlinear …

Thermal conductance of one dimensional disordered harmonic chains

We study heat conduction mediated by longitudinal phonons in one dimensional disordered harmonic chains. Using scaling properties of …

Spatiotemporal dynamics of frictional systems: The interplay of interfacial friction and bulk elasticity

Frictional interfaces are abundant in natural and engineering systems, and predicting their behavior still poses challenges of prime …

Learning data-driven discretizations for partial differential equations

The numerical solution of partial differential equations (PDEs) is challenging because of the need to resolve spatiotemporal features …

Machine learning in a data-limited regime: augmenting experiments with synthetic data uncovers order in crumpled sheets

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, …

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