Ivan Bioli, Carlo Marcati, Giancarlo Sangalli, "Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra". arXiv preprint, 2025.
Ivan Bioli, Yannis Voet, "A theoretical study on the effect of mass lumping on the discrete frequencies in immersogeometric analysis". arXiv preprint, 2024 (to appear on Journal of Scientific Computing, 2025).
Ivan Bioli, Daniel Kressner, Leonardo Robol, "Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations". SIAM Journal on Scientific Computing, 2025. Code available here.
Master's Thesis: "Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations" (2024). Advisors: Daniel Kressner (EPFL), Leonardo Robol (Università di Pisa)
COMPMAT Spring Workshop , Pavia, Italy (May 2025)
Poster: "Accelerating PINNs Training with Efficient Randomized Preconditioners"
Annual Meeting of EMS activity group on Scientific Machine Learning , Milano, Italy (Mar. 2025)
Poster: "Accelerating PINNs Training with Efficient Randomized Preconditioners"
Advanced Numerical Methods for Machine & Deep Learning, Ferrara, Italy (Jan. 2025)
Poster: "Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations"
Due Giorni di Algebra Lineare Numerica e Applicazioni @ UNIPI, Pisa, Italy (Jan. 2025)
Talk: "Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations"
PAV-IA, Università di Pavia, Pavia, Italy (Dec. 2024)
Seminar: "Bridging the gap between FEM and PINNs"
Lions-Magenes Days 2024, Pavia, Italy (May 2024)
Poster: "Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations"
SIAM Conference on Applied Linear Algebra (LA24), Paris, France (May 2024)
Poster: "Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations"
Caffè Beltrami, Università di Pavia, Pavia, Italy (Apr. 2024)
Seminar: "Introduzione all'ottimizzazione Riemanniana"
PYSANUM: Pisan Young Seminars in Applied and NUmerical Mathematics, Pisa, Italy (Apr. 2024)
Seminar: "Un'introduzione all'ottimizzazione Riemanniana con applicazioni ad equazioni matriciali"
NumPI Seminars - Numerical Analysis Group, Università di Pisa, Pisa, Italy (Apr. 2024)
Seminar: "Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations"
PINN-PAD: Physics Informed Neural Networks in PADova, Università di Padova, Padova, Italy (Feb. 2024)
Contributed Talk: "Multi-Fidelity Neural Network Surrogate Modeling for Large-Scale Bayesian Inverse Problems with Applications to Inverse Acoustic Scattering by Random Domains"
Internal Seminar, Università di Pavia, Department of Mathematics, Pavia, Italy (Jul. 2023)
Seminar: "Multi-Fidelity Surrogate Modeling for Large-Scale Bayesian Inverse Problems using Artificial Neural Networks"
SIAM Conference on Uncertainty Quantification (UQ24), Trieste, Italy (Feb. 2024)
Presenter: Dr. F. Henríquez
Talk: "Parametric Shape Holomorphy of Boundary Integral Operators: Application to Operator Learning and Multifidelity Bayesian Inversion"
WONAPDE 2024: Seventh Chilean Workshop on Numerical Analysis of Partial Differential Equations, Universidad de Concepción, Concepción, Chile (Jan. 2024)
Presenter: Dr. F. Henríquez
Talk: "Parametric Shape Holomorphy of Boundary Integral Operators: Application to Operator Learning and Multi-fidelity Bayesian Inversion"
Conference: Lombardy Young NUmerical Analysts (LYNUM VI), Pavia, Italy (7 May 2025)
Seminar/Course: Advanced Scientific Programming in Pyhton, Pavia, Italy (10-14 Mar. 2025)
Conference: Joint GNCS-SIAM Chapters Meeting for Young Researchers in Numerical Analysis and Applied Mathematics, Pavia, Italy (10-11 Feb. 2025)
Seminars cycle: PAV-IA: Beyond informal AI talks, Pavia, Italy (2024-ongoing)
Multi-Fidelity Surrogate Modeling for Large-Scale Bayesian Inverse Problems using Artificial Neural Networks (Feb. 2023 - Jun. 2023)
Semester Project under the supervision of Professor J.S. Hesthaven and Dr. F. Henríquez
Keywords: Bayesian Inverse Problems, Markov Chain Monte Carlo, Scientific Machine Learning, Data-driven
Code: Python, Pytorch, LaTeX
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems (Feb. 2022 - Jun. 2022)
Semester Project under the supervision of Professor D. Kressner and Dr. A. Cortinovis
Keywords: Computational Linear Algebra, Dynamical Systems, Koopman Operator, Data-driven techniques
Code: MATLAB, LaTeX
Training an agent to play Tic-Tac-Toe (Apr. 2022 - Jun. 2022)
Artificial Neural Networks, course project in Reinforcement Learning
Keywords: Reinforcement Learning, Q-Learning, Deep Q-Learning (DQN)
Code: Python, Keras, LaTeX
U-Net for segmenting fascicles in vagus nerve histologies (Nov. 2021 - Dec. 2021)
Machine Learning, course project
Keywords: Computer Vision, Biomedical Image Segmentation, Convolutional Neural Networks, U-Net
Code: Python, Keras, scikit-image, LaTeX
Higgs Boson Challenge (Oct. 2021)
Machine Learning, course project
Keywords: Bayesian Inverse Problems, Markov Chain Monte Carlo, Scientific Machine Learning, Data-driven
Code: Python, Pytorch, LaTeX
Rayleigh-Ritz acceleration for Subspace Iteration Method (Feb. 2021)
Scientific Computing, course project
Keywords: Machine Learning, Binary Classification
Code: Python, Numpy, LaTeX
Absolute Value Equations (AVE) (Aug. 2020 - Sept. 2020)
Computational Laboratory, course project
Keywords: Numerical Linear Algebra, Nonlinear Eigenvalues Problems
Code: MATLAB, LaTeX