Physics-based AI models such as neural networks, neural operators, and operator transformers - Aditya Seshaditya

We will leverage quantum machine learning techniques to enhance fluid dynamics simulations using physics-informed AI models and their quantum counterparts (Quantum PINNs). The workshop will explore various implementations, including attention-enhanced architectures. The goal is to demonstrate the potential of quantum computing in solving high-dimensional partial differential equations (PDEs) associated with fluid dynamics, improving both computational efficiency and accuracy.

Open-source frameworks: PennyLane (QML), NVIDIA PhysicsNemo (PINNs)

In this workshop, we will cover the following steps:

• Physics-inspired models
• Training and Loss-Physics
• Hybrid quantum-classical approaches

By the end of this workshop, you’ll understand the details of AI models used for simulations such as CFD (Computational Fluid Dynamics) and the relevance of physics-based approaches.

About the speaker:

Aditya Seshaditya is a Data Scientist with background in Quantum Computing, Digital Twins and AI.

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