7 citations · 19 across the 16 of their papers we have counts for
19 papers
DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
Erik Wikingsson, Martin Andrae, Tomas Landelius +1
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guida…
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Maria Marchenko, Martin Andrae, Fredrik Lindsten +1
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-ra…
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…
Climate Downscaling with Stochastic Interpolants (CDSI)
Erik Larsson, Ramon Fuentes-Franco, Mikhail Ivanov +1
Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain…
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…
DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
Martin Andrae, Erik Wikingsson, So Takao +2
Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. C…
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…
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Simon Adamov, Joel Oskarsson, Leif Denby +8
Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large…
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…
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal +3
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its…
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…
cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM
Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff +1
The three-dimensional structure of proteins plays a crucial role in determining their function. Protein structure prediction methods, like AlphaFold, offer rapid access to a protei…
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…