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10 Results
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Workshop
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FO-PINNs: A First-Order formulation for Physics~Informed Neural Networks Rini Jasmine Gladstone · Mohammad Amin Nabian · Hadi Meidani |
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Poster
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Thu 14:00 |
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck · Siddhartha Mishra |
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Poster
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Tue 9:00 |
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations Ramansh Sharma · Varun Shankar |
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Workshop
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Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs Ritam Majumdar · Vishal Jadhav · Anirudh Deodhar · Shirish Karande · Lovekesh Vig · Venkataramana Runkana |
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Workshop
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Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators Haydn Maust · Zongyi Li · Yixuan Wang · Anima Anandkumar |
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Workshop
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A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data Brydon Eastman · Lena Podina · Mohammad Kohandel |
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Workshop
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Fri 4:55 |
How PINNs cheat: Predicting chaotic motion of a double pendulum Sophie Steger · Franz M. Rohrhofer · Bernhard Geiger |
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Workshop
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Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks Junwoo Cho · Seungtae Nam · Hyunmo Yang · Seok-Bae Yun · Youngjoon Hong · Eunbyung Park |
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Poster
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Thu 9:00 |
PDEBench: An Extensive Benchmark for Scientific Machine Learning Makoto Takamoto · Timothy Praditia · Raphael Leiteritz · Daniel MacKinlay · Francesco Alesiani · Dirk Pflüger · Mathias Niepert |
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Workshop
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How PINNs cheat: Predicting chaotic motion of a double pendulum Sophie Steger · Franz M. Rohrhofer · Bernhard Geiger |