4 papers
A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
Francisco Caldas, Sahil Kumar, Cláudia Soares
We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such a…
OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
Alexandre Oliveira, Katarina Dyreby, Francisco Caldas +1
The increasing number of satellites and orbital debris has made space congestion a critical issue, threatening satellite safety and sustainability. Challenges such as collision avo…
Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
Katarina Dyreby, Francisco Caldas, Cláudia Soares
In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts an…
Advancing Solutions for the Three-Body Problem Through Physics-Informed Neural Networks
Manuel Santos Pereira, LuÃs Tripa, Nélson Lima +2
First formulated by Sir Isaac Newton in his work "Philosophiae Naturalis Principia Mathematica", the concept of the Three-Body Problem was put forth as a study of the motion of the…