3 papers
cs.LG2025
Automated Manifold Learning for Reduced Order Modeling
Imran Nasim, Melanie Weber
The problem of identifying geometric structure in data is a cornerstone of (unsupervised) learning. As a result, Geometric Representation Learning has been widely applied across sc…
cs.LG2024
Towards Foundation Models for the Industrial Forecasting of Chemical Kinetics
Imran Nasim, Joaõ Lucas de Sousa Almeida
Scientific Machine Learning is transforming traditional engineering industries by enhancing the efficiency of existing technologies and accelerating innovation, particularly in mod…
cs.LG2024
Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems
Imran Nasim, Joaõ Lucas de Sousa Almeida
The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machin…