Talks and presentations

  • Workshop “Deep Learning for PDE-based Inverse Problems”, 31 October 2024 - Oberwolfach, Germany. “Dynamical systems-based structured networks”, SLIDES
  • SIAM Conference on Mathematics of Data Science (MDS24), 21 October 2024 - Atlanta, USA. “Structure-Preserving Solutions of Hamiltonian Systems Based on Neural Networks”, SLIDES
  • PhD Defence Talk, 25 September 2024 - Trondheim, “Neural Networks, Differential Equations, and Structure Preservation”, SLIDES
  • PhD Defence Trial Lecture, 25 September 2024 - Trondheim, “SINDy – a survey of methods and their properties”, SLIDES
  • SciCADE, 15 July 2024 - Singapore, “Neural network aided simulation of ordinary differential equations”, SLIDES
  • Structured Machine Learning and Time–Stepping for Dynamical Systems, 20 February 2024 - Banff, Canada, “Improving the robustness of Graph Neural Networks with coupled dynamical systems”, SLIDES
  • Workshop 20 December 2023 - University of Verona, “Contractive Systems Inspired GNNs”, SLIDES
  • TES Conference on Mathematical Optimization for Machine Learning, Berlin, “Predictions Based on Pixel Data”, SLIDES
  • ICIAM 2023, Tokyo, Japan, “Structured neural networks and some applications”, SLIDES
  • ECMI Conference 2023, Wroklav, Poland, “Learning Hamiltonians of constrained mechanical systems”, SLIDES
  • FoCM 2023, Paris, 12-06-2023, “Structured neural networks and some applications to dynamical systems”, SLIDES
  • The mathematical and statistical foundation of future data-driven engineering, Newton-Institute Cambridge, 30-05-2023, SLIDES
  • CIA Seminar, Cambridge, 24-03-2023, “From neural networks to dynamical systems and back”, SLIDES
  • SIAM CS23, Amsterdam,
  • Theoretical and Computational aspects of Dynamical Systems (HB60), Trysil, Norway, 13-12-2022, “Dynamical systems’ based neural networks”, SLIDES
  • The Symbiosis of Deep Learning and Differential Equations (DLDE) - II, NeurIPS Workshop, 09-12-2022, POSTER
  • SciCADE, Reykjavík, 28-07-2022, “Structure preserving neural networks coming from ODE models”, SLIDES
  • One day – Young Researchers Seminars, Maths Applications & Models,“Learning Hamiltonians of constrained mechanical systems”, 08-07-2022 SLIDES
  • DNA Seminar NTNU, “Neural networks modelled by dynamical systems”, 20-06-2022 SLIDES
  • Math Meets Industry, Trondheim, 02-06-2022, “Structured neural networks motivated by dynamical systems”, SLIDES
  • Machine Learning and Dynamical Systems Seminar, Turing Institute, 05-05-2022, “Learnings Hamiltonians of constrained mechanical systems”, SLIDES
  • Veronesi Tutti Math Seminar, 06-04-2022, “Some connections between dynamical systems and neural networks”, SLIDES
  • MAGIC 2022, “Robustness of neural networks for classification problems”, SLIDES
  • Workshop “Computational Mathematics and Machine Learning”, “Learning the Hamiltonian of some constrained mechanical systems”: SLIDES
  • NUMDIFF-16, “Learning the Hamiltonian of some classes of mechanical systems”: SLIDES
  • DNA Seminar NTNU, “Lie group integrator’s approach to the N-fold pendulum”: SLIDES
  • MAGIC 2021, “Lie group integrator’s approach to the N-fold pendulum”: SLIDES