3 papers
math.NA2026
A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory
Dimitrios Gourzoulidis, Soumaya Elkantassi, Serafim Kalliadasis
We develop a physics-informed neural network (PINN) framework for nonlocal partial differential equations arising in dynamic density functional theory (DDFT). Such equations are ch…
stat.AP2025
Inference on the Miss Distance in a Conjunction
J. Russell Carpenter, Anthony C. Davison, Soumaya Elkantassi +1
Over the last quarter-century, spacecraft conjunction assessment has focused on a quantity associated by its advocates with collision probability. This quantity has a well-known di…
stat.AP2025
Statistical Inference on the Miss Distance Compared to Collision Probability for Conjunction Analysis
Soumaya Elkantassi, Valérie Chavez-Demoulin, Anthony C. Davison +2
Satellite conjunctions involving near misses of space objects are increasingly common, especially with the growth of satellite constellations and space debris. Accurate risk analys…