32 citations · 49 across the 8 of their papers we have counts for
8 papers
PhiPlot: A Web-Based Interactive EDA Environment for Atmospherically Relevant Molecules
Matias Loukojärvi, Ananth Mahadevan, Katsiaryna Haitsiukevich +1
Advances in computational chemistry have produced high-dimensional datasets on atmospherically relevant molecules. To aid exploration of such datasets, particularly for the study o…
Information Hidden in Gradients of Regression with Target Noise
Arash Jamshidi, Katsiaryna Haitsiukevich, Kai Puolamäki
Second-order information -- such as curvature or data covariance -- is critical for optimisation, diagnostics, and robustness. However, in many modern settings, only the gradients…
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich +3
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading…
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
Katsiaryna Haitsiukevich, Onur Poyraz, Pekka Marttinen +1
This paper explores the efficacy of diffusion-based generative models as neural operators for partial differential equations (PDEs). Neural operators are neural networks that learn…
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese +1
State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. In this work…
Learning Trajectories of Hamiltonian Systems with Neural Networks
Katsiaryna Haitsiukevich, Alexander Ilin
Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions t…
Improved Training of Physics-Informed Neural Networks with Model Ensembles
Katsiaryna Haitsiukevich, Alexander Ilin
Learning the solution of partial differential equations (PDEs) with a neural network is an attractive alternative to traditional solvers due to its elegance, greater flexibility an…
A Grid-Structured Model of Tubular Reactors
Katsiaryna Haitsiukevich, Samuli Bergman, Cesar de Araujo Filho +2
We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which descri…