AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box
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AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification

29 September 2023

Nazanin Ahmadi Daryakenari

Mario De Florio

George Karniadakis

Papers citing "AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification"

8 / 8 papers shown

Title
Physics Informed Constrained Learning of Dynamics from Static Data Pengtao Dang Tingbo Guo Melissa Fishel Guang Lin Wenzhuo Wu Sha Cao Chi Zhang PINN AI4CE 49 0 0 17 Apr 2025
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification Siva Viknesh Younes Tatari Amirhossein Arzani 21 1 0 21 Oct 2024
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling Vivek Oommen Aniruddha Bora Zhen Zhang George Karniadakis DiffM 45 13 0 13 Sep 2024
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl M. Cranmer 41 38 0 02 May 2023
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines Kamaljyoti Nath Xuhui Meng Daniel J. Smith George Karniadakis PINN 10 20 0 26 Apr 2023
Symbolic Regression is NP-hard M. Virgolin S. Pissis 57 58 0 03 Jul 2022
Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach Evangelos Galaris Gianluca Fabiani I. Gallos Ioannis G. Kevrekidis Constantinos Siettos AI4CE 19 40 0 31 Jan 2022
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 503 0 11 Mar 2020