:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Longhi, Alessandro, Lathouwers, Danny, Perkó, Zoltán
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.08683
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Angle dependent dose transformer algorithm for fast proton therapy dose calculations
by: Stryja, Mikołaj, et al.
Published: (2026)

Yet anOther Dose Algorithm (YODA) for independent computations of dose and dose changes due to anatomical changes
by: Burlacu, Tiberiu, et al.
Published: (2024)

A deep learning model for inter-fraction head and neck anatomical changes
by: Burlacu, Tiberiu, et al.
Published: (2024)

Modified Equations for Stochastic Optimization
by: Perko, Stefan
Published: (2025)

Learning Physics Informed Neural ODEs With Partial Measurements
by: Ghanem, Paul, et al.
Published: (2024)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
by: Perko, Stefan
Published: (2025)

BULL-ODE: Bullwhip Learning with Neural ODEs and Universal Differential Equations under Stochastic Demand
by: Naik, Nachiket N., et al.
Published: (2025)

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
by: Liu, Zewen, et al.
Published: (2025)

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

Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)

Probabilistic Proton Treatment Planning: a novel approach for optimizing underdosage and overdosage probabilities of target and organ structures
by: de Jong, Jelte R., et al.
Published: (2025)

Equivariant Manifold Neural ODEs and Differential Invariants
by: Andersdotter, Emma, et al.
Published: (2024)

Layer Separation Deep Learning Model with Auxiliary Variables for Partial Differential Equations
by: Liu, Yaru, et al.
Published: (2025)

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
by: Shi, Dai, et al.
Published: (2026)

CompNO: A Novel Foundation Model approach for solving Partial Differential Equations
by: Hmida, Hamda, et al.
Published: (2026)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
by: Liang, Senwei, et al.
Published: (2022)

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)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
by: Ahmad, Zan, et al.
Published: (2024)

A minimax optimal control approach for robust neural ODEs
by: Cipriani, Cristina, et al.
Published: (2023)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)

Disentangled Representation Learning for Parametric Partial Differential Equations
by: Liu, Ning, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equations
by: Trifonov, Vladislav, et al.
Published: (2024)

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
by: Abid, Muhammad, et al.
Published: (2026)

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

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

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

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

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
by: Hardwick, Gareth, et al.
Published: (2024)

Accelerating Neural ODEs Using Model Order Reduction
by: Lehtimäki, Mikko, et al.
Published: (2021)

From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
by: Cipriani, Cristina, et al.
Published: (2023)

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

Solving Partial Differential Equations with Equivariant Extreme Learning Machines
by: Harder, Hans, et al.
Published: (2024)

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
by: Yang, Qihong, et al.
Published: (2026)

GeoMaNO: Geometric Mamba Neural Operator for Partial Differential Equations
by: Han, Xi, et al.
Published: (2025)

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

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

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)