7 citations · 19 across the 16 of their papers we have counts for
12 papers · 1 filter
Interacting particle guidance for sampling reward-tilted generative priors
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund +1
Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or samples with desired properties…
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for oc…
Particle-Guided Diffusion Models for Partial Differential Equations
Andrew Millard, Fredrik Lindsten, Zheng Zhao
We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals…
ESS-Flow: Training-free guidance of flow-based models as inference in source space
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund +1
Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on…
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions,…
Discriminative Ordering Through Ensemble Consensus
Louis Ohl, Fredrik Lindsten
Evaluating the performance of clustering models is a challenging task where the outcome depends on the definition of what constitutes a cluster. Due to this design, current existin…
Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts prim…
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designi…
Continuous Ensemble Weather Forecasting with Diffusion models
Martin Andrae, Tomas Landelius, Joel Oskarsson +1
Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent w…
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth +1
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on determinist…
Discriminator Guidance for Autoregressive Diffusion Models
Filip Ekström Kelvinius, Fredrik Lindsten
We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuo…
Graph-based Neural Weather Prediction for Limited Area Modeling
Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having acces…