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DPM: A Novel Training Method for Physics-Informed Neural Networks in
  Extrapolation

DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation

AAAI Conference on Artificial Intelligence (AAAI), 2020
4 December 2020
Jungeun Kim
Kookjin Lee
Dongeun Lee
Sheo Yon Jin
Noseong Park
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation"

33 / 33 papers shown
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
Juan Diego Toscano
Daniel T. Chen
George Karniadakis
381
3
0
03 Dec 2025
Neuro-Spectral Architectures for Causal Physics-Informed Networks
Neuro-Spectral Architectures for Causal Physics-Informed Networks
Arthur Bizzi
Leonardo M. Moreira
Márcio Marques
Leonardo Mendonça
Christian Júnior de Oliveira
...
Daniel Yukimura
Pavel Petrov
João M. Pereira
Tiago Novello
Lucas Nissenbaum
PINN
402
2
0
05 Sep 2025
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero
Carmine Delle Femine
Kenji S. Muro
M. Quartulli
Marcello Restelli
PINNAI4CE
185
1
0
29 Aug 2025
Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics
Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics
Alex Guo
Michael D. Graham
AI4CE
249
0
0
21 Jul 2025
Recurrent Neural Operators: Stable Long-Term PDE Prediction
Recurrent Neural Operators: Stable Long-Term PDE Prediction
Zaijun Ye
Chen-Song Zhang
Wansheng Wang
AI4CE
289
8
0
27 May 2025
Dual-Balancing for Physics-Informed Neural Networks
Dual-Balancing for Physics-Informed Neural NetworksInternational Joint Conference on Artificial Intelligence (IJCAI), 2025
Chenhong Zhou
Jie Chen
Zaifeng Yang
Ching Eng Png
PINNAI4CE
453
7
0
16 May 2025
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
Qishen Zhou
Yifan Zhang
Michail A. Makridis
Anastasios Kouvelas
Yibing Wang
Simon Hu
351
2
0
20 Mar 2025
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing NetworksTransportation Research Part C: Emerging Technologies (TRC), 2025
Qishen Zhou
Yifan Zhang
Michail A. Makridis
Anastasios Kouvelas
Yibing Wang
Simon Hu
AI4TS
426
11
0
21 Jan 2025
Frequency-adaptive Multi-scale Deep Neural Networks
Frequency-adaptive Multi-scale Deep Neural NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2024
Jizu Huang
Rukang You
Tao Zhou
AI4CE
389
21
0
28 Sep 2024
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
Dual Cone Gradient Descent for Training Physics-Informed Neural NetworksNeural Information Processing Systems (NeurIPS), 2024
Youngsik Hwang
Dong-Young Lim
AI4CE
476
12
0
27 Sep 2024
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Parameterized Physics-informed Neural Networks for Parameterized PDEsInternational Conference on Machine Learning (ICML), 2024
Woojin Cho
Minju Jo
Haksoo Lim
Kookjin Lee
Dongeun Lee
Sanghyun Hong
Noseong Park
PINNAI4CE
269
49
1
18 Aug 2024
Fast training of accurate physics-informed neural networks without gradient descent
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar
Taniya Kapoor
Abhishek Chandra
Q. Sun
Erik Lien Bolager
Iryna Burak
Anna Veselovska
Massimo Fornasier
Felix Dietrich
PINNAI4CE
410
5
0
31 May 2024
Neural Networks-based Random Vortex Methods for Modelling Incompressible
  Flows
Neural Networks-based Random Vortex Methods for Modelling Incompressible Flows
Vladislav Cherepanov
Sebastian W. Ertel
165
0
0
22 May 2024
Robust Physics Informed Neural Networks
Robust Physics Informed Neural Networks
Marcin Lo's
Maciej Paszyñski
PINN
349
0
0
04 Jan 2024
Personalized Predictions of Glioblastoma Infiltration: Mathematical
  Models, Physics-Informed Neural Networks and Multimodal Scans
Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans
Ray Zirui Zhang
Ivan Ezhov
Michal Balcerak
Andy Zhu
Benedikt Wiestler
Bjoern Menze
John S. Lowengrub
AI4CE
253
22
0
28 Nov 2023
Transfer learning for improved generalizability in causal
  physics-informed neural networks for beam simulations
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulationsEngineering applications of artificial intelligence (Eng. Appl. Artif. Intell.), 2023
Taniya Kapoor
Hongrui Wang
Alfredo Núñez
R. Dollevoet
AI4CEPINN
227
37
0
01 Nov 2023
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural
  Networks
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
Woojin Cho
Kookjin Lee
Donsub Rim
Noseong Park
AI4CEPINN
310
45
0
14 Oct 2023
Neural oscillators for generalization of physics-informed machine
  learning
Neural oscillators for generalization of physics-informed machine learningAAAI Conference on Artificial Intelligence (AAAI), 2023
Taniya Kapoor
Abhishek Chandra
D. Tartakovsky
Hongrui Wang
Alfredo Núñez
R. Dollevoet
AI4CE
291
17
0
17 Aug 2023
GRINN: A Physics-Informed Neural Network for solving hydrodynamic
  systems in the presence of self-gravity
GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity
Sayantan Auddy
Ramit Dey
N. Turner
S. Basu
PINNAI4CE
252
9
0
15 Aug 2023
Understanding and Mitigating Extrapolation Failures in Physics-Informed
  Neural Networks
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks
Lukas Fesser
Luca DÁmico-Wong
Richard Qiu
373
9
0
15 Jun 2023
Efficient Error Certification for Physics-Informed Neural Networks
Efficient Error Certification for Physics-Informed Neural NetworksInternational Conference on Machine Learning (ICML), 2023
Francisco Eiras
Adel Bibi
Rudy Bunel
Krishnamurthy Dvijotham
Juil Sock
M. P. Kumar
PINN
430
7
0
17 May 2023
Enabling Hard Constraints in Differentiable Neural Network and
  Accelerator Co-Exploration
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-ExplorationDesign Automation Conference (DAC), 2022
Deokki Hong
Kanghyun Choi
Hyeyoon Lee
Joonsang Yu
Noseong Park
Youngsok Kim
Jinho Lee
168
4
0
23 Jan 2023
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative
  Filtering
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Seoyoung Hong
Minju Jo
Seung-Uk Kook
Jaeeun Jung
Hyowon Wi
Noseong Park
Sung-Bae Cho
AI4TS
211
7
0
08 Nov 2022
A Unified Hard-Constraint Framework for Solving Geometrically Complex
  PDEs
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEsNeural Information Processing Systems (NeurIPS), 2022
Songming Liu
Zhongkai Hao
Chengyang Ying
Hang Su
Jun Zhu
Ze Cheng
AI4CE
428
28
0
06 Oct 2022
Implicit Neural Spatial Representations for Time-dependent PDEs
Implicit Neural Spatial Representations for Time-dependent PDEsInternational Conference on Machine Learning (ICML), 2022
Honglin Chen
Rundi Wu
E. Grinspun
Changxi Zheng
Julius Berner
AI4CE
548
52
0
30 Sep 2022
Modelling of physical systems with a Hopf bifurcation using mechanistic
  models and machine learning
Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learningMechanical systems and signal processing (MSSP), 2022
K. H. Lee
David A.W. Barton
L. Renson
233
15
0
07 Sep 2022
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes
  Equations
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes EquationsThe Physics of Fluids (Phys. Fluids), 2022
Rui Zhang
Tailin Wu
Qi Meng
Yue Wang
Rongchan Zhu
Bingguang Chen
Zhi-Ming Ma
Tie-Yan Liu
306
18
0
20 Jun 2022
Physical Activation Functions (PAFs): An Approach for More Efficient
  Induction of Physics into Physics-Informed Neural Networks (PINNs)
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)Neurocomputing (Neurocomputing), 2022
J. Abbasi
Paal Ostebo Andersen
PINNAI4CE
319
42
0
29 May 2022
Enhanced Physics-Informed Neural Networks with Augmented Lagrangian
  Relaxation Method (AL-PINNs)
Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs)Neurocomputing (Neurocomputing), 2022
Hwijae Son
S. Cho
H. Hwang
PINN
276
81
0
29 Apr 2022
When Physics Meets Machine Learning: A Survey of Physics-Informed
  Machine Learning
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng
Sungyong Seo
Defu Cao
Sam Griesemer
Yan Liu
PINNAI4CE
403
190
0
31 Mar 2022
Physics-Informed Graph Learning
Physics-Informed Graph Learning
Ciyuan Peng
Xiwei Xu
Vidya Saikrishna
Huan Liu
PINNAI4CE
342
9
0
22 Feb 2022
Scientific Machine Learning through Physics-Informed Neural Networks:
  Where we are and What's next
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextJournal of Scientific Computing (J. Sci. Comput.), 2022
S. Cuomo
Vincenzo Schiano Di Cola
F. Giampaolo
G. Rozza
Maizar Raissi
F. Piccialli
PINN
656
2,104
0
14 Jan 2022
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal
  Patterns with an Autoregressive Embedding Loss
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
Konstantin Klemmer
Tianlin Xu
Beatrice Acciaio
Daniel B. Neill
AI4TS
331
18
0
30 Sep 2021
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