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Main Authors:	Zong, Yifei, Barajas-Solano, David, Tartakovsky, Alexandre M.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.04617
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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
by: Zeng, Jice, et al.
Published: (2024)

Statistical Learning Analysis of Physics-Informed Neural Networks
by: Barajas-Solano, David A.
Published: (2026)

VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations
by: Zong, Yifei, et al.
Published: (2025)

Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models
by: Wang, Yuanzhe, et al.
Published: (2024)

Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
by: Propp, Adrienne M., et al.
Published: (2024)

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems
by: Venkatasubramanian, Subashree, et al.
Published: (2024)

Neural Incremental Data Assimilation
by: Blanke, Matthieu, et al.
Published: (2024)

Bayesian Reasoning for Physics Informed Neural Networks
by: Graczyk, Krzysztof M., et al.
Published: (2023)

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
by: Ramirez, Ibai, et al.
Published: (2025)

Sparsely-Supervised Data Assimilation via Physics-Informed Schrödinger Bridge
by: Bu, Dohyun, et al.
Published: (2026)

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
by: Zeng, Jice, et al.
Published: (2026)

Learning Hamiltonian Dynamics with Bayesian Data Assimilation
by: Kim, Taehyeun, et al.
Published: (2025)

$Φ$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation
by: Glyn-Davies, Alex, et al.
Published: (2022)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025)

Causality and Bayesian network PDEs for multiscale representations of porous media
by: Um, Kimoon, et al.
Published: (2019)

Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
by: Rohrhofer, Franz M., et al.
Published: (2021)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
by: Makrygiorgos, Georgios, et al.
Published: (2025)

Deep Bayesian Filter for Bayes-faithful Data Assimilation
by: Tarumi, Yuta, et al.
Published: (2024)

Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
by: Zhao, Yuxuan, et al.
Published: (2026)

Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation Consistency in Dynamical Models
by: Tian, Xiaoxu
Published: (2024)

Randomized Geometric Algebra Methods for Convex Neural Networks
by: Wang, Yifei, et al.
Published: (2024)

Data Subsampling for Bayesian Neural Networks
by: Kawasaki, Eiji, et al.
Published: (2022)

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
by: Chen, Kun, et al.
Published: (2024)

On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
by: Becerra-Zuniga, Nicolás, et al.
Published: (2026)

Conformalized Physics-Informed Neural Networks
by: Podina, Lena, et al.
Published: (2024)

Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
by: Jiang, Hongjie, et al.
Published: (2026)

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
by: Kong, Lingfei, et al.
Published: (2026)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)

$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
by: Figueres, Júlia Vicens, et al.
Published: (2025)

Topological Approach for Data Assimilation
by: Chumley, Max M., et al.
Published: (2024)

Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
by: Mohammad-Djafari, Ali, et al.
Published: (2025)

Singular Bayesian Neural Networks
by: Toure, Mame Diarra, et al.
Published: (2026)

Meta-Learning Fourier Neural Operators for Hessian Inversion and Enhanced Variational Data Assimilation
by: Moazzami, Hamidreza, et al.
Published: (2025)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)

Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)

Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)

Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors
by: Beauchamp, Maxime, et al.
Published: (2024)

Complex Physics-Informed Neural Network
by: Si, Chenhao, et al.
Published: (2025)

Baseflow identification via explainable AI with Kolmogorov-Arnold networks
by: Liu, Chuyang, et al.
Published: (2024)

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