Physics Informed Neural Networks in Padova (PINN-PAD)
PADOVA, 22-23 February 2024
PROGRAM
Thursday, February 22 – Aula Nievo – Palazzo Bo
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| 08.55 → 09.00 | Opening Remarks
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| 09.00 → 10.00 | Anna Schwarz | Recent advances and failures in the machine-learning enhanced solution of PDEs |
| 10.00 → 10.50 | CT1 Y. Saleh | Spectral learning for solving molecular Schrödinger equations |
CT2 M. Tanveer | Neural Network Approach to Learn Delay Differential Equations via Pseudospectral Collocation |
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| 10.50 → 11.20 | Coffee Break at caffè Pedrocchi
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| 11.20 → 12.20 | Francesco Dalla Santa | Graph-informed neural network and discontinuity learning |
| 12.20 → 13.10 | CT3 E. Chinellato | Physics-Aware Deep Nonnegative Matrix Factorization |
CT4 R. Boiger | Solving the Bateman Equation using Physics Informed Neural Networks |
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| 13.10 → 15.00 | Lunch (not provided)
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| 15.00 → 16.00 | Gianluigi Rozza | Accelerating Numerical Simulations by Model Reduction with Scientific and Physics-Informed Machine Learning |
| 16.00 → 16.50 | CT5 G. A. D’Inverno | Physics Informed Graph Neural Networks for AC Optimal Power Flow |
CT6 A. Jnini | Gauss-Newton Natural Gradient for Physics-Informed Computational Fluid Dynamics |
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| 16.50 → 17.20 | Coffee Break at caffè Pedrocchi
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| 17.20 → 18.20 | Salvatore Cuomo | Computational Paradigms in Scientific Machine Learning |
| 20.30 | Social dinner at Restaurant:
Isola di Caprera, via Marsilio da Padova, 11 | |
Friday, February 23 – Aula E Giurisprudenza – Palazzo Bo
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| 09.00 → 10.00 | Federica Bragone | Physics-Informed Neural Networks for Power Systems Applications |
| 10.00 → 11.15 | CT7 F. Difonzo | Physics Informed Neural Networks for an Inverse Problem in Peridynamic Models |
CT8 A. Forootani | Application of Physics-Informed Neural Networks in Nonlinear Systems Identification and Parameter Estimation |
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CT9 M. Hoefler | Parameter estimation in cardiac biomechanical models based on physics-informed neural networks |
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| 11.15 → 11.45 | Coffee Break at caffè Pedrocchi
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| 11.45 → 13.00 | CT10 A. Lovison | Brain memory working. Optimal control behavior for improved Hopfield-like models |
CT11 F. J. Barraza Henriquez | Wavenumber-Robust Deep ReLU Neural Network Emulation in Acoustic Wave Scattering |
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CT12 F. Marchetti | Predicting coronal mass ejections’ travel times by using physics-informed loss functions |
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| 13.00 → 14.45 | Lunch (not provided)
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| 14.45 → 15.45 | Paola Antonietti | Machine Learning-enhanced Polytopal Finite Element Methods |
| 15.45 → 16.10 | CT13 I. Bioli | Multi-Fidelity Neural Network Surrogate Modeling for Large-Scale Bayesian Inverse Problems |
| 16.10 → 16.15 | Concluding Remarks
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