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Control and Deep Learning: Some connections

B. Geshkovski, E. Zuazua.  Control and Deep Learning: Some connections (2021)

This note is an extended abstract for a talk given by the second author during the workshop ”Challenges in Optimization with Complex PDE-Systems”, at Oberwolfach, in February 2021.

It is superfluous to state the impact that deep learning has had on modern technology, as it powers many tools of modern society, ranging from web search to content filtering on social networks. A key paradigm of deep learning is that of supervised learning, which may be seen as a compound and high-dimensional simultaneous control problem. This is the viewpoint adopted by our group. And here we present some of our main findings.

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Last updated on March 17, 2022

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Last Publications

Optimal actuator design via Brunovsky’s normal form

Stability and Convergence of a Randomized Model Predictive Control Strategy

Slow decay and Turnpike for Infinite-horizon Hyperbolic LQ problems

Control of certain parabolic models from biology and social sciences

Relaxation approximation and asymptotic stability of stratified solutions to the IPM equation

  • Postdoc at DASEL project -Open position
  • FAU MoD Lecture: Applications of AAA Rational Approximation
  • DASEL
  • Optimal actuator design via Brunovsky’s normal form
  • ERC DyCon Impact Dimension (2016-2022)
  • Postdoc at DASEL project -Open position
  • FAU MoD Lecture: Applications of AAA Rational Approximation
  • DASEL
  • Optimal actuator design via Brunovsky’s normal form
  • ERC DyCon Impact Dimension (2016-2022)
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