Search Results for author:
Found 12 papers, 1 papers with code
Multirotor Ensemble Model Predictive Control I: Simulation Experiments
Nonlinear receding horizon model predictive control is a powerful approach to controlling nonlinear dynamical systems.
A Stochastic Compound Failure Model for Testing Resilience of Autonomous Fixed-Wing Aircraft I: Formulation and Simulation
This paper presents a Markov chain model to dynamically emulate the effects of adverse (failure) flight conditions on fixed-wing, autonomous aircraft system actuators.
Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data
Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses.
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning
Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies.
An Adaptive Digital Autopilot for Fixed-Wing Aircraft with Actuator Faults
This paper develops an adaptive digital autopilot for a fixed-wing aircraft and compares its performance with a fixed-gain autopilot.
Learn Like The Pro: Norms from Theory to Size Neural Computation
The optimal design of neural networks is a critical problem in many applications.
Informative Neural Ensemble Kalman Learning
In stochastic systems, informative approaches select key measurement or decision variables that maximize information gain to enhance the efficacy of model-related inferences.
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades.
The Sloop System for Individual Animal Identification with Deep Learning
The MIT Sloop system indexes and retrieves photographs from databases of non-stationary animal population distributions.
Neural Networks as Geometric Chaotic Maps
The use of artificial neural networks as models of chaotic dynamics has been rapidly expanding.
Neural Integration of Continuous Dynamics
Using the polynomial class of dynamical systems, we demonstrate the equivalence of neural and numerical integration.
Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet.
