:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pereira, Manuel Santos, Tripa, Luís, Lima, Nélson, Caldas, Francisco, Soares, Cláudia
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2503.04585
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
by: Dyreby, Katarina, et al.
Published: (2025)

Machine Learning in Orbit Estimation: a Survey
by: Caldas, Francisco, et al.
Published: (2022)

Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
by: Caldas, Francisco, et al.
Published: (2024)

A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
by: Caldas, Francisco, et al.
Published: (2026)

OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
by: Oliveira, Alexandre, et al.
Published: (2025)

Enhancing Physics-Informed Neural Networks Through Feature Engineering
by: Fazliani, Shaghayegh, et al.
Published: (2025)

Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
by: Rohrhofer, Franz M., et al.
Published: (2024)

Generative Design of Periodic Orbits in the Restricted Three-Body Problem
by: Gil, Alvaro Francisco, et al.
Published: (2024)

Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
by: Lotfalian, Amin, et al.
Published: (2025)

Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
by: Lahoud, Alan A., et al.
Published: (2024)

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

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
by: Jekic, Aleksandra, et al.
Published: (2025)

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

Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise
by: Lu, Albert, et al.
Published: (2024)

Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks
by: Liu, Shuheng, et al.
Published: (2025)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
by: Bylinkin, Dmitry, et al.
Published: (2025)

Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
by: Vatellis, Vasileios
Published: (2024)

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts
by: Rodriguez, Arturo, 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)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)

Complex Physics-Informed Neural Network
by: Si, Chenhao, 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)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
by: Mazandarani, Mehran, et al.
Published: (2025)

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

Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
by: Hoffmann, Matthias K., et al.
Published: (2025)

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
by: Flores, Pablo, et al.
Published: (2025)

Physical Informed Neural Networks for modeling ocean pollutant
by: Battina, Karishma, et al.
Published: (2025)

Fourier Feature Pyramids for Physics-Informed Neural Networks
by: Zhao, Brandon, et al.
Published: (2026)

Functional Tensor Decompositions for Physics-Informed Neural Networks
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)

Architectural Strategies for the optimization of Physics-Informed Neural Networks
by: Saratchandran, Hemanth, et al.
Published: (2024)

The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks
by: Bonfanti, Andrea, et al.
Published: (2024)

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics
by: Wei, Amaury, et al.
Published: (2024)

Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
by: Alam, Md Mahbub, et al.
Published: (2025)

A Prefixed Patch Time Series Transformer for Two-Point Boundary Value Problems in Three-Body Problems
by: Hatakeyama, Akira, et al.
Published: (2025)

Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)

Densely Multiplied Physics Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2024)

Floating-Body Hydrodynamic Neural Networks
by: Zhang, Tianshuo, et al.
Published: (2025)