6 citations · 12 across the 11 of their papers we have counts for
11 papers
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial on Si memristors via Dynamic Spectral Optimization
Rodion Podorozhny, Nikoleta Theodoropoulou, Jelena Tešić
Physics-informed neural networks (PINNs) offer a promising framework for modeling semiconductor devices, yet standard architectures struggle with severe numerical stiffness and mul…
Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration
Sylvain V. Costes, Sergio Garcia Busto, Ryan T. Scott +15
While AI holds the potential to revolutionize space life sciences, realizing this promise is contingent upon the systematic restructuring of heterogeneous spaceflight biological da…
Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs
Ehsan Zeraatkar, Rodion Podorozhny, Jelena Tešić
Reconstructing continuous physical fields from sparse, irregular observations is a central challenge in scientific machine learning, particularly for systems governed by partial di…
Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models
Ehsan Zeraatkar, Salah A Faroughi, Jelena Tešić
Super-resolution can play an essential role in enhancing the spatial fidelity of Earth System Model outputs, allowing fine-scale structures highly beneficial to climate science to…
ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models
Ehsan Zeraatkar, Salah Faroughi, Jelena Tešić
Purpose: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide…
KAN KAN Buff Signed Graph Neural Networks?
Muhieddine Shebaro, Jelena Tešić
Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neura…
GraphC: Parameter-free Hierarchical Clustering of Signed Graph Networks v2
Muhieddine Shebaro, Lucas Rusnak, Martin Burtscher +1
Spectral clustering methodologies, when extended to accommodate signed graphs, have encountered notable limitations in effectively encapsulating inherent grouping relationships. Re…
Scaling Frustration Index and Corresponding Balanced State Discovery for Real Signed Graphs
Muhieddine Shebaro, Jelena Tešić
Structural balance modeling for signed graph networks presents how to model the sources of conflicts. The state-of-the-art focuses on computing the frustration index of a signed gr…
ABCD: Algorithm for Balanced Component Discovery in Signed Networks
Muhieddine Shebaro, Jelena Tešić
The largest balanced element in signed graphs plays a vital role in helping researchers understand the fundamental structure of the graph, as it reveals valuable information about…
Advances in Scaling Community Discovery Methods for Signed Graph Networks
Maria Tomasso, Lucas Rusnak, Jelena Tešić
Community detection is a common task in social network analysis (SNA) with applications in a variety of fields including medicine, criminology, and business. Despite the popularity…
Characterizing Attitudinal Network Graphs through Frustration Cloud
Lucas Rusnak, Jelena Tešić
Attitudinal Network Graphs are signed graphs where edges capture an expressed opinion; two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). A s…