I am thrilled to announce my upcoming webinar, hosted by the IEEE Signal Processing Society, where I’ll discuss how the integration of Artificial Intelligence and physics can open new doors in scientific research. In recent years, AI has flourished, particularly with deep learning (DL) algorithms becoming increasingly prevalent. However, while DL has driven innovation across many sectors, scientific problems present unique challenges that require more than a standard “plug-and-play” approach.
In this session, we’ll explore why a one-size-fits-all AI solution is insufficient for scientific applications. The lack of interpretability and application-specific optimization in current DL approaches has highlighted the need for integrating the physics of each scientific domain into AI solutions. This not only enhances performance but also ensures that AI tools are fail-safe and scientifically sound.
I will delve into a variety of strategies to combine physics with AI and DL, sharing examples from cutting-edge research that demonstrate the impact of this integrated approach. By embedding the principles of physics directly into AI models, we can achieve greater accuracy, reliability, and scientific explainability in areas ranging from complex data analysis to predictive modeling.
Webinar Details:
- Date: October 2, 2024
- Time: 8:00 AM – 9:00 AM
- Host: IEEE Signal Processing Society
- Speaker: Dr. Krishna Agarwal
For anyone interested in the transformative potential of AI in scientific contexts, I encourage you to register and join the conversation. Together, we’ll explore how a physics-guided approach can redefine the way we use AI to address scientific challenges.
Register for the webinar here: IEEE Webinar Registration
Thank you for reading, and I look forward to connecting with you there!