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Analysis of biologically plausible neuron models for regression with spiking neural networks
Mario De Florio, Adar Kahana, George Em Karniadakis
This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized f…
Learning thermoacoustic interactions in combustors using a physics-informed neural network
Sathesh Mariappan, Kamaljyoti Nath, George Em Karniadakis
We introduce a physics-informed neural network (PINN) method to study thermoacoustic interactions leading to combustion instability in combustors. Specifically, we employ a PINN to…
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression
Mario De Florio, Ioannis G. Kevrekidis, George Em Karniadakis
Discovering mathematical models that characterize the observed behavior of dynamical systems remains a major challenge, especially for systems in a chaotic regime. The challenge is…
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
Hanxun Jin, Enrui Zhang, Boyu Zhang +3
Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-an…
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen, Khemraj Shukla, Saaketh Desai +2
Direct numerical simulations (DNS) are accurate but computationally expensive for predicting materials evolution across timescales, due to the complexity of the underlying evolutio…
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science
Chenxi Wu, Alan John Varghese, Vivek Oommen +1
The new polymath Large Language Models (LLMs) can speed-up greatly scientific reviews, possibly using more unbiased quantitative metrics, facilitating cross-disciplinary connection…