Copertina

Adaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nooraiepour, Mohammad, Masoudi, Mohammad, Song, Zezhang, Hellevang, Helge
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2026)

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2026)

Carbon mineralization in CO2-seawater-basalt systems: Reactive transport dynamics and vesicular pore architecture controls
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2026)

Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
di: Nooraiepour, Mohammad
Pubblicazione: (2026)

Intricacies of CO2-Basalt Interactions, Reactive Flow and Carbon Mineralization: Bridging Numerical Forecasts to Empirical Realities
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2024)

Geological CO2 storage assessment in emerging CCS regions: Review of sequestration potential, policy development, and socio-economic factors in Poland
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2025)

Self-Enhancing Halite Growth Creates Secondary Porous Networks During CO2 Storage in Saline Aquifers
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2025)

Bayesian Parametric Matrix Models: Principled Uncertainty Quantification for Spectral Learning
di: Nooraiepour, Mohammad
Pubblicazione: (2025)

Microfluidic Study of Evaporation-Driven Crystallization of Saline and Ammonia Brines under Hydrogen Flow
di: Dąbrowski, Karol M., et al.
Pubblicazione: (2025)

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

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

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

Microfluidic studies of Salt Precipitation: Influence of Brine Composition, Interfacial Tension, Flow Conditions, and Chemical Additives
di: Dąbrowski, Karol M., et al.
Pubblicazione: (2025)

Quantifying Salt Precipitation During CO2 Injection: How Flow Rate, Temperature, and Phase State Control Near-Wellbore Crystallization
di: Dąbrowski, Karol M., et al.
Pubblicazione: (2026)

Assessing salt precipitation and weak acid interaction in subsurface CO2 injection: Potential 50% strength decline in near-wellbore reservoir sandstones
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2024)

How does surface wettability alter salt precipitation and growth dynamics during CO$_2$ injection into saline aquifers: A microfluidic analysis
di: Dąbrowski, Karol M., et al.
Pubblicazione: (2024)

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
di: Podina, Lena, et al.
Pubblicazione: (2024)

Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
di: Abedi, Mohammad Mahdi, et al.
Pubblicazione: (2025)

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

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

Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2026)

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

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

Physics-Informed Neural Networks with Unknown Partial Differential Equations: an Application in Multivariate Time Series
di: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Pubblicazione: (2025)

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks
di: Podina, Lena, et al.
Pubblicazione: (2024)

Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
di: Habich, Tim-Lukas, et al.
Pubblicazione: (2025)

Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
di: Aljubran, Mohammad J., et al.
Pubblicazione: (2024)

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
di: Wu, Yulun, et al.
Pubblicazione: (2025)

Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks
di: Shahbandari, Lida, et al.
Pubblicazione: (2024)

Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators
di: Khaniki, Mohammad Ali Labbaf, et al.
Pubblicazione: (2024)

Replay-Based Continual Learning for Physics-Informed Neural Operators
di: Wang, Yizheng, et al.
Pubblicazione: (2026)

A Green-Integral-Constrained Neural Solver with Stochastic Physics-Informed Regularization
di: Abedi, Mohammad Mahdi, et al.
Pubblicazione: (2026)

Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
di: Arni, Rohan, et al.
Pubblicazione: (2025)

Predicting Question Quality on StackOverflow with Neural Networks
di: Al-Ramahi, Mohammad, et al.
Pubblicazione: (2024)

Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
di: Doost, Mohammad, et al.
Pubblicazione: (2025)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
di: Zeng, Chengxi, et al.
Pubblicazione: (2024)

Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
di: McClenny, Levi, et al.
Pubblicazione: (2020)

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

Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
di: Wang, Yizheng, et al.
Pubblicazione: (2025)

X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation
di: Nabian, Mohammad Amin, et al.
Pubblicazione: (2024)