GPT welcome message: Hello! I'm here to help with physics-informed neural networks, integrating insights from expert blogs and foundational research.

Sample prompts:

How to sample collocation points?

Methods that can improve the stability of PINN training?

What does causal training mean in PINN?

Future opportunities for applying active learning to PINN?

AuthorSHUAI GUO

Generated by ChatGPT

The PINN Design Pattern Specialist is a Generative Pre-training Transformer (GPT) developed with the key aim of offering professional assistance and expertise in physics-informed neural networks (PINNs).

This GPT introduces and underscores the essential integration of physics-based perspectives and neural network technology, bringing together theoretical understandings from blogs and foundational research.

The PINN Design Pattern Specialist is capable of addressing queries about various aspects of PINNs, such as methods enhancing the stability of PINN training, the implication of causal training within PINNs and practical approaches to sample collocation points - fundamental to model accuracy.

Not confined to providing instructional assistance, this GPT also delves into the implications of PINNs for future opportunities, offering insights into the potentiality of applying active-learning methodologies to PINNs.To engage with the PINN Design Pattern Specialist, users are required to have access to ChatGPT Plus.

Through an interactive interface, this GPT converses with the user, providing in-depth responses to inquiries and shedding light on the multi-dimensional utilities of PINNs.

Covering a broad range of PINN-related topics, it can be quintessential for researchers, data scientists, AI practitioners, or anyone interested in the fusion of neural networks with physics-based principles.