7 citations · 9 across the 6 of their papers we have counts for
6 papers
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Nikolaos Evangelou, Tianqi Cui, Juan M. Bello-Rivas +2
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We…
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
Gianluca Fabiani, Nikolaos Evangelou, Tianqi Cui +4
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipp…
Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors
Tianqi Cui, Tom S. Bertalan, Nelson Ndahiro +4
Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reacti…
Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information
Saurabh Malani, Tom S. Bertalan, Tianqi Cui +3
Experimental data is often comprised of variables measured independently, at different sampling rates (non-uniform t between successive measurements); and at a specific time poi…
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Tianqi Cui, Thomas Bertalan, George J. Pappas +3
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output. Conversely, there…
Initializing LSTM internal states via manifold learning
Felix P. Kemeth, Tom Bertalan, Nikolaos Evangelou +3
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of LSTM recurrent neural networks, ensuring consistency wi…