Fri 4:00 a.m. - 4:10 a.m.
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Introduction and opening remarks
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Opening remarks
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SlidesLive Video
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Fri 4:10 a.m. - 4:25 a.m.
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Provable Active Learning of Neural Networks for Parametric PDEs
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Spotlight
)
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link
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Aarshvi Gajjar · Chinmay Hegde · Christopher Musco
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Fri 4:25 a.m. - 4:40 a.m.
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PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
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Spotlight
)
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link
SlidesLive Video
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Namgyu Kang · Byeonghyeon Lee · Youngjoon Hong · Seok-Bae Yun · Eunbyung Park
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Fri 4:40 a.m. - 4:55 a.m.
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Bridging the Gap Between Coulomb GAN and Gradient-regularized WGAN
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Spotlight
)
>
link
SlidesLive Video
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Siddarth Asokan · Chandra Seelamantula
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Fri 4:55 a.m. - 5:10 a.m.
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How PINNs cheat: Predicting chaotic motion of a double pendulum
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Spotlight
)
>
link
SlidesLive Video
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Sophie Steger · Franz M. Rohrhofer · Bernhard Geiger
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Fri 5:10 a.m. - 6:05 a.m.
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Poster Session 1
(
Poster Session 1
)
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Fri 6:05 a.m. - 6:50 a.m.
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Keynote Talk 1
(
Keynote Talk 1
)
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SlidesLive Video
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Yang Song
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Fri 6:50 a.m. - 7:05 a.m.
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Blind Drifting: Diffusion models with a linear SDE drift term for blind image restoration tasks
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Spotlight
)
>
link
SlidesLive Video
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Simon Welker · Henry Chapman · Timo Gerkmann
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Fri 7:05 a.m. - 8:05 a.m.
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Break
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Fri 8:05 a.m. - 8:50 a.m.
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Keynote Talk 2
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Keynote Talk 2
)
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Rose Yu
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Fri 8:50 a.m. - 9:05 a.m.
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A Universal Abstraction for Hierarchical Hopfield Networks
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Spotlight
)
>
link
SlidesLive Video
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Benjamin Hoover · Duen Horng Chau · Hendrik Strobelt · Dmitry Krotov
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Fri 9:05 a.m. - 10:00 a.m.
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Poster Session 2
(
Poster Session 2
)
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Fri 10:00 a.m. - 10:45 a.m.
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Keynote Talk 3
(
Keynote Talk 3
)
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Christopher Rackauckas
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Fri 10:45 a.m. - 10:55 a.m.
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Closing remarks
(
Closing remarks
)
>
SlidesLive Video
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On the impact of larger batch size in the training of Physics Informed Neural Networks
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Poster
)
>
link
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Shyam Sankaran · Hanwen Wang · Leonardo Ferreira Guilhoto · Paris Perdikaris
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PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
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Poster
)
>
link
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Moshe Eliasof · Eldad Haber · Eran Treister
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A Neural ODE Interpretation of Transformer Layers
(
Poster
)
>
link
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Yaofeng Zhong · Tongtao Zhang · Amit Chakraborty · Biswadip Dey
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-
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Provable Active Learning of Neural Networks for Parametric PDEs
(
Poster
)
>
link
|
Aarshvi Gajjar · Chinmay Hegde · Christopher Musco
🔗
|
-
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PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
(
Poster
)
>
link
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Namgyu Kang · Byeonghyeon Lee · Youngjoon Hong · Seok-Bae Yun · Eunbyung Park
🔗
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-
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Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
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Poster
)
>
link
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Junwoo Cho · Seungtae Nam · Hyunmo Yang · Seok-Bae Yun · Youngjoon Hong · Eunbyung Park
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LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla’s LiFePo4 EV Battery
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Poster
)
>
link
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Jeyhun Rustamov · Luisa Fennert · Nico Hoffmann
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Neural Latent Dynamics Models
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Poster
)
>
link
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Nicola Farenga · Stefania Fresca · Andrea Manzoni
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Optimal Control of PDEs Using Physics-Informed Neural Networks
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Poster
)
>
link
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Saviz Mowlavi · Saleh Nabi
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Physics Informed Symbolic Networks
(
Poster
)
>
link
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Ritam Majumdar · Vishal Jadhav · Anirudh Deodhar · Shirish Karande · Lovekesh Vig · Venkataramana Runkana
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Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems
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Poster
)
>
link
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Shuheng Liu · Xiyue Huang · Pavlos Protopapas
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Learning flows of control systems
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Poster
)
>
link
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Miguel Aguiar · Amritam Das · Karl H. Johansson
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-
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Bridging the Gap Between Coulomb GAN and Gradient-regularized WGAN
(
Poster
)
>
link
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Siddarth Asokan · Chandra Seelamantula
🔗
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-
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Efficient Robustness Verification of Neural Ordinary Differential Equations
(
Poster
)
>
link
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Mustafa Zeqiri · Mark Müller · Marc Fischer · Martin Vechev
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Solving Singular Liouville Equations Using Deep Learning
(
Poster
)
>
link
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Yuxiang Ji
🔗
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How PINNs cheat: Predicting chaotic motion of a double pendulum
(
Poster
)
>
link
|
Sophie Steger · Franz M. Rohrhofer · Bernhard Geiger
🔗
|
-
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Structure preserving neural networks based on ODEs
(
Poster
)
>
link
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Davide Murari · Elena Celledoni · Brynjulf Owren · Carola-Bibiane Schönlieb · Ferdia Sherry
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-
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Blind Drifting: Diffusion models with a linear SDE drift term for blind image restoration tasks
(
Poster
)
>
link
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Simon Welker · Henry Chapman · Timo Gerkmann
🔗
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-
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Learned 1-D advection solver to accelerate air quality modeling
(
Poster
)
>
link
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Manho Park · Zhonghua Zheng · Nicole Riemer · Christopher Tessum
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Learning Ordinary Differential Equations with the Line Integral Loss Function
(
Poster
)
>
link
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Albert Johannessen
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A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data
(
Poster
)
>
link
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Brydon Eastman · Lena Podina · Mohammad Kohandel
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-
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A Universal Abstraction for Hierarchical Hopfield Networks
(
Poster
)
>
link
|
Benjamin Hoover · Duen Horng Chau · Hendrik Strobelt · Dmitry Krotov
🔗
|
-
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Modular Flows: Differential Molecular Generation
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Poster
)
>
link
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Yogesh Verma · Samuel Kaski · Markus Heinonen · Vikas Garg
🔗
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Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
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Poster
)
>
link
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Guillaume Morel · Lucas Drumetz · Nicolas Courty · François Rousseau · Simon Benaïchouche
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Numerical integrators for learning dynamical systems from noisy data
(
Poster
)
>
link
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Håkon Noren · Sølve Eidnes · Elena Celledoni
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Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling
(
Poster
)
>
link
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Alexandre Allauzen · Thiago Petrilli Maffei Dardis · Hannah De Oliveira Plath
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Hamiltonian Neural Koopman Operator
(
Poster
)
>
link
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Jingdong Zhang · Qunxi Zhu · Wei LIN
🔗
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torchode: A Parallel ODE Solver for PyTorch
(
Poster
)
>
link
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Marten Lienen · Stephan Günnemann
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