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Main Authors:	Mortezanejad, Seyedeh Azadeh Fallah, Wang, Ruochen, Mohammad-Djafari, Ali
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2503.20144
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Signed Rank Chart For Tied Observations: An Application of Deep Learning Models
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025)

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

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

Model Based and Physics Informed Deep Learning Neural Network Structures
by: Mohammad-Djafari, Ali, et al.
Published: (2024)

Dependence control chart using maximum copula entropy
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2020)

Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)
by: Mohammad-Djafari, Ali
Published: (2026)

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
by: Gupta, Aarush, et al.
Published: (2025)

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
by: Makki, Maedeh, et al.
Published: (2026)

Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
by: Xiao, Xiongye, et al.
Published: (2023)

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026)

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
by: Farea, Afrah, et al.
Published: (2024)

Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
by: Gupta, Deepak, et al.
Published: (2025)

Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
by: Miyatake, Luis Kin, et al.
Published: (2025)

Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Fractional Differential Equation Physics-Informed Neural Network and Its Application in Battery State Estimation
by: Dang, Lujuan, et al.
Published: (2025)

Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation
by: Zhang, Bo, et al.
Published: (2024)

Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
by: Koumpanakis, Michail, et al.
Published: (2024)

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
by: Matthews, Jason, et al.
Published: (2024)

Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network
by: Lin, Zhongshuo, et al.
Published: (2025)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations
by: Park, Keon Vin
Published: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
by: Rajaei, Pedram, et al.
Published: (2025)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

Universal Time Series Generation with Neural Controlled Differential Equations
by: Berndt, Torben, et al.
Published: (2026)

Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
by: Iyer, Narayan S, et al.
Published: (2025)

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
by: Ben-Yair, Ido, et al.
Published: (2021)

GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
by: Forootani, Ali, et al.
Published: (2024)

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
by: Soni, Juhi, et al.
Published: (2025)

ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
by: Cai, Wanlin, et al.
Published: (2024)

Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
by: Nagesh, Chandra Kanth, et al.
Published: (2025)

Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations
by: Chen, Yuan, et al.
Published: (2025)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
by: Yang, Yahong
Published: (2024)

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
by: Hao, Wenrui, et al.
Published: (2024)

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