Frank Schäfer

Frank Schäfer

PhD candidate in Physics

Bruder group, University of Basel

Biography

I am a third-year PhD candidate in physics in the Bruder group within the “Quantum Computing and Quantum Technology” PhD school at the University of Basel. I have participated in the Google Summer of Code (GSoC) 2020 program with the project “High weak order stochastic differential equation solvers and their utility in neural stochastic differential equations” within the Julia Language organization. The project was supervised by Chris Rackauckas, Moritz Schauer, and Yingbo Ma. Currently, I am working on my GSoC 2021 project “Neural Hybrid Differential Equations and Adjoint Sensitivity Analysis” within the NumFocus organization. My GSoC 2021 mentors are Chris Rackauckas, Moritz Schauer, Yingbo Ma, and Mohamed Tarek. Since 2020, I am a member of the SciML open source software organization for scientific machine learning.

Interests

  • Scientific machine learning
  • Differentiable programming
  • Automatic differentiation
  • Neural ODEs/SDEs
  • Quantum optimal control
  • Parameter inference
  • ML for phase transitions
  • Quantum optics
  • Many-body physics
  • Multiple scattering theory

Education

  • PhD candidate under the supervision of Prof. Dr. Christoph Bruder, 2018 - present

    Department of Physics, University of Basel

  • MSc in Physics under the supervision of Prof. Dr. Andreas Buchleitner, 2018

    Department of Physics, Albert-Ludwigs-Universität Freiburg

  • BSc in Physics under the supervision of PD Dr. Thomas Wellens, 2015

    Department of Physics, Albert-Ludwigs-Universität Freiburg

Posts

Neural Hybrid Differential Equations

I am delighted that I have been awarded my second GSoC stipend this year. I look forward to carrying out the ambitious project scope with my mentors Chris Rackauckas, Moritz Schauer, Yingbo Ma, and Mohamed Tarek.

High weak order solvers and adjoint sensitivity analysis for stochastic differential equations

Project summary In this project, we have implemented new promising tools within the SciML organization which are relevant for tasks such as optimal control or parameter estimation for stochastic differential equations.

High weak order SDE solvers

This post summarizes our new high weak order methods for the SciML ecosystem, as implemented within the Google Summer of Code 2020 project. After an introductory part highlighting the differences between the strong and the weak approximation for stochastic differential equations, we look into the convergence and performance properties of a few representative new methods in case of a non-commutative noise process.

GSoC 2020: High weak order SDE solvers and their utility in neural SDEs

First and foremost, I would like to thank my mentors Chris Rackauckas, Moritz Schauer, and Yingbo Ma for their willingness to supervise me in this Google Summer of Code project. Although we are still at the very beginning of the project, we already had plenty of very inspiring discussion.

Research projects

Control of (Stochastic) Quantum Dynamics with Differentiable Programming

Quantum control based on parametrized controllers trained with gradient information computed by (adjoint) sensitivity methods.

Machine Learning for Phase Transitions

Data-driven methods based on sample instances of the state of a physical system as a function of the system’s parameters.

Spectral Structure and Many-Body Dynamics of Ultracold Bosons in a Double Well

Study of the spectral structure and the resulting dynamics of a few bosons under consideration of different initial conditions.

Cooperative Scattering of Scalar Waves by Optimized Configurations of Point Scatterers

Numerical optimization of the positions of point scatterers to maximize the total scattering cross section for an incoming plane wave.

Open source software

SciML Scientific Machine Learning Software

Contributions to the SciML ecosystem in Julia, especially the DiffEqSensitivity.jl package for sensitivity analysis utilities, the StochasticDiffEq.jl package for stochastic differential equations solvers, and the DiffEqNoiseProcess package for tools to develop noise processes for differential equations.

MitosisStochasticDiffEq

Implementation of the backward filter and the forward change of measure of the Automatic Backward Filtering Forward Guiding paradigm. Joint work with Moritz Schauer.