Response Dynamic
Response dynamics research focuses on understanding and predicting how systems react to various stimuli, encompassing diverse fields from material science to social behavior and machine learning. Current research emphasizes developing accurate and generalizable models, employing techniques like neural networks (including dual-path and convolutional architectures), graph-based frameworks, and Markov chains to capture complex, often nonlinear, response patterns. These advancements improve prediction accuracy across various applications, from designing advanced materials and optimizing advertising strategies to enhancing the safety and reliability of autonomous systems and mitigating biases in AI models.
Papers
Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function
Ismail Erbas, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Amit Verma, Margarida Barosso, Xavier Intes
Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments
Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara, Daichi Takeda