Cover Image

Particle-Guided Diffusion Models for Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Millard, Andrew, Lindsten, Fredrik, Zhao, Zheng
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling
by: Millard, Andrew, et al.
Published: (2026)

Discriminator Guidance for Autoregressive Diffusion Models
by: Kelvinius, Filip Ekström, et al.
Published: (2023)

Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
by: Kelvinius, Filip Ekström, et al.
Published: (2025)

Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
by: Millard, Andrew, et al.
Published: (2026)

Towards Understanding Epoch-wise Double descent in Two-layer Linear Neural Networks
by: Olmin, Amanda, et al.
Published: (2024)

ESS-Flow: Training-free guidance of flow-based models as inference in source space
by: Kalaivanan, Adhithyan, et al.
Published: (2025)

Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
by: Larsson, Erik, et al.
Published: (2025)

On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
by: Govindarajan, Hariprasath, et al.
Published: (2024)

Prior Learning in Introspective VAEs
by: Athanasiadis, Ioannis, et al.
Published: (2024)

Continuous Ensemble Weather Forecasting with Diffusion models
by: Andrae, Martin, et al.
Published: (2024)

Discriminative Ordering Through Ensemble Consensus
by: Ohl, Louis, et al.
Published: (2025)

Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
by: Millard, Andrew, et al.
Published: (2025)

Fast and Scalable Score-Based Kernel Calibration Tests
by: Glaser, Pierre, et al.
Published: (2025)

CRPS-LAM: Regional ensemble weather forecasting from matching marginals
by: Larsson, Erik, et al.
Published: (2025)

On the connection between Noise-Contrastive Estimation and Contrastive Divergence
by: Olmin, Amanda, et al.
Published: (2024)

Active Learning with Weak Supervision for Gaussian Processes
by: Olmin, Amanda, et al.
Published: (2022)

cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM
by: Ducrocq, Gabriel, et al.
Published: (2024)

Elements of Sequential Monte Carlo
by: Naesseth, Christian A., et al.
Published: (2019)

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
by: Oskarsson, Joel, et al.
Published: (2024)

Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
by: Gao, Han, et al.
Published: (2024)

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
by: Kelvinius, Filip Ekström, et al.
Published: (2025)

QUEST: A robust attention formulation using query-modulated spherical attention
by: Govindarajan, Hariprasath, et al.
Published: (2026)

DINO as a von Mises-Fisher mixture model
by: Govindarajan, Hariprasath, et al.
Published: (2024)

Latent Mamba Operator for Partial Differential Equations
by: Tiwari, Karn, et al.
Published: (2025)

Self-supervised Pretraining for Partial Differential Equations
by: Madhavan, Varun, et al.
Published: (2024)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
by: Holmberg, Daniel, et al.
Published: (2026)

DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
by: Andrae, Martin, et al.
Published: (2025)

Second-Order Tensorial Partial Differential Equations on Graphs
by: Einizade, Aref, et al.
Published: (2025)

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

Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
by: Ding, Yifan, et al.
Published: (2025)

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

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

Base Models for Parabolic Partial Differential Equations
by: Xu, Xingzi, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, 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)

Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
by: Millard, Andrew, et al.
Published: (2025)

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

Multi-scale Time-stepping of Partial Differential Equations with Transformers
by: Hemmasian, AmirPouya, et al.
Published: (2023)

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