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Found 14 papers, 13 papers with code
Control of Medical Digital Twins with Artificial Neural Networks
A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time.
Metamodeling and Control of Medical Digital Twins
Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions.
Gradient-free training of neural ODEs for system identification and control using ensemble Kalman inversion
Ensemble Kalman inversion (EKI) is a sequential Monte Carlo method used to solve inverse problems within a Bayesian framework.
Visualizing high-dimensional loss landscapes with Hessian directions
We show that saddle points in the original space are rarely correctly identified as such in expected lower-dimensional representations if random projections are used.
Near-optimal control of dynamical systems with neural ordinary differential equations
Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state $\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time $T$.
Mathematical Characterization of Private and Public Immune Repertoire Sequences
Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions.
Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems
We propose a solution to such problems by combining two classes of numerical methods: (i) adaptive spectral methods and (ii) physics-informed neural networks (PINNs).
Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
To solve such optimization problems, inventory managers need to decide what quantities to order from each supplier, given the net inventory and outstanding orders, so that the expected backlogging, holding, and sourcing costs are jointly minimized.
Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
Efficient testing and vaccination protocols are critical aspects of epidemic management.
Implicit energy regularization of neural ordinary-differential-equation control
Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable.
Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities
We find that the average excess death across the entire US is 13$\%$ higher than the number of reported COVID-19 deaths.
From classical to quantum walks with stochastic resetting on networks
We study the influence of quantum effects on the stationary and long-time average probability distribution by interpolating between the classical and quantum regime.
Statistical Mechanics Quantum Physics
Neural Ordinary Differential Equation Control of Dynamics on Graphs
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs).
Learning the Ising Model with Generative Neural Networks
We also find that convolutional layers in VAEs are important to model spin correlations whereas RBMs achieve similar or even better performances without convolutional filters.
