Pre-trained machine learning (ML) models have shown great performance for awide range
of applications, in particular in natural language processing (NLP) and computer vision (CV) …

In various engineering and applied science applications, repetitive numerical simulations of
partial differential equations (PDEs) for varying input parameters are often required (eg …

Complex physical systems are often described by partial differential equations (PDEs) that
depend on parameters such as the Reynolds number in fluid mechanics. In applications …

Can we build a single large model for a wide range of PDE-related scientific learning tasks?
Can this model generalize to new PDEs without any fine-tuning? In-context operator …

The application of physics-informed neural networks (PINNs) is investigated for the first time
in solving the one-dimensional countercurrent spontaneous imbibition (COUCSI) problem at …

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for
understanding tumor growth dynamics and designing personalized radiotherapy treatment …

Neural networks have emerged as a tool for solving differential equations in many branches
of engineering and science. But their progress in frequency domain acoustics is limited by …

AI for PDEs has garnered significant attention, particularly Physics-Informed Neural
Networks (PINNs). However, PINNs are typically limited to solving specific problems, and …

Deep learning models trained on finite data lack a complete understanding of the physical
world. On the other hand, physics-informed neural networks (PINNs) are infused with such …

Academicians and researchers have been keen on climatic and maritime monitoring.
Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean …