32 citations · 83 across the 11 of their papers we have counts for
11 papers
Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
Peter Yatsyshin, Karl Lapo, Jonathan Smith +4
Antarctic sea ice has undergone unprecedented changes in recent years, raising questions about how this key geophysical system is responding to climate change. Decades of slow expa…
Learning Density Functionals to Bridge Particle and Continuum Scales
Edoardo Monti, Peter Yatsyshin, Konstantinos Gkagkas +1
Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provide…
Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0
Eric G. Daub, Tom Dunstan, Thusal Bennett +30
We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan…
FastNet: Improving the physical consistency of machine-learning weather prediction models through loss function design
Tom Dunstan, Oliver Strickson, Thusal Bennett +31
Machine learning weather prediction (MLWP) models have demonstrated remarkable potential in delivering accurate forecasts at significantly reduced computational cost compared to tr…
Deep Optimal Sensor Placement for Black Box Stochastic Simulations
Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin +1
Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a…
Physics-informed Bayesian inference of external potentials in classical density-functional theory
Antonio Malpica-Morales, Peter Yatsyshin, Miguel A. Duran-Olivencia +1
The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functio…
Generalizing Impermanent Loss on Decentralized Exchanges with Constant Function Market Makers
Rohan Tangri, Peter Yatsyshin, Elisabeth A. Duijnstee +1
Liquidity providers are essential for the function of decentralized exchanges to ensure liquidity takers can be guaranteed a counterparty for their trades. However, liquidity provi…
Physics-constrained Bayesian inference of state functions in classical density-functional theory
Peter Yatsyshin, Serafim Kalliadasis, Andrew B. Duncan
We develop a novel data-driven approach to the inverse problem of classical statistical mechanics: given experimental data on the collective motion of a classical many-body system,…
A Finite-Volume Method for Fluctuating Dynamical Density Functional Theory
Antonio Russo, Sergio P. Perez, Miguel A. Durán-Olivencia +3
We introduce a finite-volume numerical scheme for solving stochastic gradient-flow equations. Such equations are of crucial importance within the framework of fluctuating hydrodyna…
Mixing--Demixing Transition in Polymer-Grafted Spherical Nanoparticles
Peter Yatsyshin, Nikolaos G. Fytas, Panagiotis E. Theodorakis
Polymer-grafted nanoparticles (PGNPs) can provide property profiles than cannot be obtained individually by polymers or nanoparticles (NPs). Here, we have studied the mixing--demix…
Pseudospectral methods for density functional theory in bounded and unbounded domains
Andreas Nold, Benjamin D. Goddard, Peter Yatsyshin +2
Classical Density Functional Theory (DFT) is a statistical-mechanical framework to analyze fluids, which accounts for nanoscale fluid inhomogeneities and non-local intermolecular i…