8 citations · 13 across the 7 of their papers we have counts for
8 papers
Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow
Changhun Kim, Timon Conrad, Redwanul Karim +7
Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imply physically consistent sol…
Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction
Redwanul Karim, Changhun Kim, Timon Conrad +7
Accurate AC power flow (AC-PF) prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-voltage (HV) networks. Existing physi…
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim +6
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of sce…
A Hybrid ABM-PDE Framework for Real-World Infectious Disease Simulations
Kristina Kehrer, Tim O. F. Conrad
This paper presents a hybrid modeling approach that couples an Agent-Based Model (ABM) with a partial differential equation (PDE) model in an epidemic setting to simulate the spati…
Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach
Inan Bostanci, Tim Conrad
This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examin…
Vahana.jl -- A framework (not only) for large-scale agent-based models
Steffen Fürst, Tim Conrad, Carlo Jaeger +1
Agent-based models (ABMs) offer a powerful framework for understanding complex systems. However, their computational demands often become a significant barrier as the number of age…