Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Wirnsberger, Peter"'
Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising Diffusion Models. A well known limitation of Denoising Score Matching
Externí odkaz:
http://arxiv.org/abs/2402.08667
We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinat
Externí odkaz:
http://arxiv.org/abs/2305.13233
Autor:
Lam, Remi, Sanchez-Gonzalez, Alvaro, Willson, Matthew, Wirnsberger, Peter, Fortunato, Meire, Alet, Ferran, Ravuri, Suman, Ewalds, Timo, Eaton-Rosen, Zach, Hu, Weihua, Merose, Alexander, Hoyer, Stephan, Holland, George, Vinyals, Oriol, Stott, Jacklynn, Pritzel, Alexander, Mohamed, Shakir, Battaglia, Peter
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical
Externí odkaz:
http://arxiv.org/abs/2212.12794
Publikováno v:
2nd AI4Science Workshop at the 39th International Conference on Machine Learning (ICML), 2022
In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy. However, these
Externí odkaz:
http://arxiv.org/abs/2210.00612
Autor:
Wirnsberger, Peter, Papamakarios, George, Ibarz, Borja, Racanière, Sébastien, Ballard, Andrew J., Pritzel, Alexander, Blundell, Charles
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Hel
Externí odkaz:
http://arxiv.org/abs/2111.08696
A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations, like images, using priors informed by Hamiltonian mechanics. While these models have important potential applications in areas like robotics or
Externí odkaz:
http://arxiv.org/abs/2111.05986
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need to reason about a physical system using high dimensional observations,
Externí odkaz:
http://arxiv.org/abs/2111.05458
Autor:
Wirnsberger, Peter, Ballard, Andrew J., Papamakarios, George, Abercrombie, Stuart, Racanière, Sébastien, Pritzel, Alexander, Rezende, Danilo Jimenez, Blundell, Charles
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences, and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sam
Externí odkaz:
http://arxiv.org/abs/2002.04913
Publikováno v:
J. Chem. Phys. 150, 134501 (2019)
When fluids of anisotropic molecules are placed in temperature gradients, the molecules may align themselves along the gradient: this is called thermo-orientation. We discuss the theory of this effect in a fluid of particles that interact by a spheri
Externí odkaz:
http://arxiv.org/abs/1901.07240
Publikováno v:
Phys. Rev. Lett. 120, 226001 (2018)
We present a mean-field theory to explain the thermo-orientation effect in an off-centre Stockmayer fluid. This effect is the underlying cause of thermally induced polarisation and thermally induced monopoles, which have recently been predicted theor
Externí odkaz:
http://arxiv.org/abs/1804.03624