Imagen de Portada

Fast Regression of the Tritium Breeding Ratio in Fusion Reactors

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mánek, Petr, Van Goffrier, Graham, Gopakumar, Vignesh, Nikolaou, Nikolaos, Shimwell, Jonathan, Waldmann, Ingo
Formato:	Preprint
Publicado:	2021
Materias:	Computational Physics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data-Efficient Neural Operator Training via Physics-Based Active Learning
por: Polanska, Alicja, et al.
Publicado: (2026)

Differential geometry with extreme eigenvalues in the positive semidefinite cone
por: Mostajeran, Cyrus, et al.
Publicado: (2023)

Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations
por: Panayis, James, et al.
Publicado: (2025)

Geometric statistics with subspace structure preservation for SPD matrices
por: Mostajeran, Cyrus, et al.
Publicado: (2024)

An Improved Precision Calculation of the $0νββ$ Contact Term within Chiral Effective Field Theory
por: Van Goffrier, Graham
Publicado: (2024)

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
por: Tahseen, Tara P. A., et al.
Publicado: (2025)

Calibrated Physics-Informed Uncertainty Quantification
por: Gopakumar, Vignesh, et al.
Publicado: (2025)

Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
por: Nikolaou, Konstantin, et al.
Publicado: (2026)

Collective variables of neural networks: empirical time evolution and scaling laws
por: Tovey, Samuel, et al.
Publicado: (2024)

Guaranteed prediction sets for functional surrogate models
por: Gray, Ander, et al.
Publicado: (2025)

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning
por: Kim, Jinsu, et al.
Publicado: (2024)

An Empirical Investigation of Neural ODEs and Symbolic Regression for Dynamical Systems
por: Ioannou, Panayiotis, et al.
Publicado: (2026)

Adaptive Online Emulation for Accelerating Complex Physical Simulations
por: Tahseen, Tara P. A., et al.
Publicado: (2025)

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
por: Riva, Stefano, et al.
Publicado: (2025)

Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach
por: Nathan, R. P., et al.
Publicado: (2025)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
por: Padmanabha, Govinda Anantha, et al.
Publicado: (2024)

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
por: Cheng, Lixue, et al.
Publicado: (2022)

BARMPy: Bayesian Additive Regression Models Python Package
por: Van Boxel, Danielle
Publicado: (2024)

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
por: Heimbach, Lothar, et al.
Publicado: (2025)

Quantum-Assisted Hilbert-Space Gaussian Process Regression
por: Farooq, Ahmad, et al.
Publicado: (2024)

PINGS: Physics-Informed Neural Network for Fast Generative Sampling
por: Prasha, Achmad Ardani, et al.
Publicado: (2025)

Understanding team collapse via probabilistic graphical models
por: Nikolaou, Iasonas, et al.
Publicado: (2024)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
por: Lin, Yuchao, et al.
Publicado: (2025)

Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
por: Xie, YuQing, et al.
Publicado: (2026)

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions
por: Goswami, Rohit, et al.
Publicado: (2025)

Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems
por: Du, Yutong, et al.
Publicado: (2026)

Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
por: Anagnostopoulos, Sokratis J., et al.
Publicado: (2026)

TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication
por: Vanc, Petr, et al.
Publicado: (2025)

AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks
por: Laurelli, Michele
Publicado: (2024)

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
por: Svensson, David, et al.
Publicado: (2025)

Plasma Surrogate Modelling using Fourier Neural Operators
por: Gopakumar, Vignesh, et al.
Publicado: (2023)

Automated discovery of finite volume schemes using Graph Neural Networks
por: Garnier, Paul, et al.
Publicado: (2025)

QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis
por: Kumari, Subhangi, et al.
Publicado: (2026)

Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
por: Lee, Youngkyu, et al.
Publicado: (2024)

Machine Learning-Assisted Discovery of Flow Reactor Designs
por: Savage, Tom, et al.
Publicado: (2023)

Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics
por: Gorard, Jonathan, et al.
Publicado: (2025)

CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
por: Dionelis, Nikolaos, et al.
Publicado: (2025)

Active Learning in Symbolic Regression with Physical Constraints
por: Medina, Jorge, et al.
Publicado: (2023)

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
por: Yang, Steven, et al.
Publicado: (2025)

Bayesian Multistate Bennett Acceptance Ratio Methods
por: Ding, Xinqiang
Publicado: (2023)