2 citations · 2 across the 12 of their papers we have counts for
12 papers
Quantum algorithms for stochastic nonlinear differential equations
Sergey Bravyi, Adam Byrne, Mykhaylo Zayats +1
Stochastic nonlinear dynamics underlie many models in engineering and computational physics, yet accurate high-dimensional simulation remains challenging. We present a quantum algo…
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Tobia Boschi, Andrea Loreti, Nicola C. Amorisco +13
We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous d…
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado +13
Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors…
Quantum simulation of a noisy classical nonlinear dynamics
Sergey Bravyi, Robert Manson-Sawko, Mykhaylo Zayats +1
We present an end-to-end quantum algorithm for simulating nonlinear dynamics described by a system of stochastic dissipative differential equations with a quadratic nonlinearity. T…
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Jana Vatter, Mykhaylo Zayats, Marcos Martínez Galindo +4
With the ever-growing size of real-world graphs, numerous techniques to overcome resource limitations when training Graph Neural Networks (GNNs) have been developed. One such appro…
Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
Kyongmin Yeo, Małgorzata J. Zimoń, Mykhaylo Zayats +1
A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged ove…
Information Flow in Graph Neural Networks: A Clinical Triage Use Case
Víctor Valls, Mykhaylo Zayats, Alessandra Pascale
Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. However, efficient trai…
A recommender for the management of chronic pain in patients undergoing spinal cord stimulation
Tigran Tchrakian, Mykhaylo Zayats, Alessandra Pascale +7
Spinal cord stimulation (SCS) is a therapeutic approach used for the management of chronic pain. It involves the delivery of electrical impulses to the spinal cord via an implanted…
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Hoang Thanh Lam, Marco Luca Sbodio, Marcos Martínez Galindo +6
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases o…
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
Mykhaylo Zayats, Małgorzata J. Zimoń, Kyongmin Yeo +1
We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture…
Detectability and global observer design for 2D Navier-Stokes equations with uncertain inputs
Sergiy Zhuk, Mykhaylo Zayats, Emilia Fridman
We present simulation friendly detectability conditions for 2D Navier-Stokes Equation (NSE) with periodic boundary conditions, and describe a generic class of ``detectable'' observ…
Localised sequential state estimation for advection dominated flows with non-Gaussian uncertainty description
Emanuele Ragnoli, Mykhaylo Zayats, Fearghal O'Donncha +1
This paper presents a new iterative state estimation algorithm for advection dominated flows with non-Gaussian uncertainty description of -type: uncertain initial conditi…