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Automatic differentiation (AD) has become ubiquitous throughout scientific computing and deep learning.

Control of devices at the quantum level holds enormous potential for current and future applications in the field of quantum information science.

Project summary In this project, we have implemented state-of-the-art sensitivity tools for chaotic dynamical systems, continuous adjoint sensitivity methods for hybrid differential equations, as well as a high level API for automatic differentiation.

Differentiable programming (∂P), i.e., the ability to differentiate general computer program structures, has enabled the efficient combination of existing packages for scientific computation and machine learning1.

In this post, we discuss sensitivity analysis of differential equations with state changes caused by events triggered at defined moments, for example reflections, bounces off a wall or other sudden forces.

In this post, we dig into sensitivity analysis of chaotic systems.

I am delighted that I have been awarded my second GSoC stipend this year.

Conceptually, it is straightforward to determine the time evolution of a quantum system for a fixed initial state given its (time-dependent) Hamiltonian or Lindbladian.

Artificial neural networks have successfully been used to identify phase transitions from data and classify data into distinct phases in an automated fashion.

We examine the spectral structure and many-body dynamics of two and three repulsively interacting bosons trapped in a one-dimensional double-well, for variable barrier height, inter-particle interaction strength, and initial conditions.

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.

This post summarizes our new high weak order methods for the SciML ecosystem, as implemented within the Google Summer of Code 2020 project.

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.

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