Edward Ott
YOU?
Author Swipe
View article: Prediction Beyond the Medium Range With an Atmosphere‐Ocean Model That Combines Physics‐Based Modeling and Machine Learning
Prediction Beyond the Medium Range With an Atmosphere‐Ocean Model That Combines Physics‐Based Modeling and Machine Learning Open
This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics‐based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022, http…
View article: Tailored Forecasting from Short Time Series via Meta-learning
Tailored Forecasting from Short Time Series via Meta-learning Open
Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly challenging for systems with limited history. To overcome this…
View article: Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning
Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning Open
This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), whi…
View article: Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing Open
Recent work has shown that machine learning (ML) models can skillfully forecast the dynamics of unknown chaotic systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics ("…
View article: A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component
A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component Open
It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This …
View article: Deviations From the Random Plane Wave Field Distribution in Electromagnetic Enclosures
Deviations From the Random Plane Wave Field Distribution in Electromagnetic Enclosures Open
Wave energy distribution within enclosures with irregular boundaries is a common phenomenon in many branches of electromagnetics. If the wavelength of the injected wave is small compared with the structure size, the scattering properties o…
View article: Network inference from short, noisy, low time-resolution, partial measurements: Application to <i>C. elegans</i> neuronal calcium dynamics
Network inference from short, noisy, low time-resolution, partial measurements: Application to <i>C. elegans</i> neuronal calcium dynamics Open
Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements o…
View article: A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component
A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component Open
A hybrid system combining an AGCM with a machine-learning component can capture processes not captured by the AGCM.• Machine learning provides a flexible framework to introduce additional prognostic variables into the hybrid model.• The pr…
View article: Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization Open
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical pat…
View article: Time Domain Generalization of the Random Coupling Model and Experimental Verification in a Complex Scattering System
Time Domain Generalization of the Random Coupling Model and Experimental Verification in a Complex Scattering System Open
Electromagnetic (EM) wave scattering in electrically large, irregularly shaped, environments is a common phenomenon. The deterministic, or first principles, study of this process is usually computationally expensive and the results exhibit…
View article: Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems Open
In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an unknown (or partially unknown), non-stationary, potentially…
View article: Short-wavelength reverberant wave systems for physical realization of reservoir computing
Short-wavelength reverberant wave systems for physical realization of reservoir computing Open
Machine learning (ML) has found widespread application over a broad range of\nimportant tasks. To enhance ML performance, researchers have investigated\ncomputational architectures whose physical implementations promise compactness,\nhigh-…
View article: Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks Open
Forecasting the dynamics of large, complex, sparse networks from previous time series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topo…
View article: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model
A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model Open
This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approa…
View article: Deep-Learning Estimation of Complex Reverberant Wave Fields with a Programmable Metasurface
Deep-Learning Estimation of Complex Reverberant Wave Fields with a Programmable Metasurface Open
Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging, particularly in reverberating environments. T…
View article: Eigenfunction and eigenmode-spacing statistics in chaotic photonic crystal graphs
Eigenfunction and eigenmode-spacing statistics in chaotic photonic crystal graphs Open
The statistical properties of wave chaotic systems of varying dimensionalities and realizations have been studied extensively. These systems are commonly characterized by the statistics of the eigenmode-spacings and the statistics of the e…
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances in Modeling Earth Systems (JAMES). Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may …
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances in Modeling Earth Systems (JAMES). Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may …
View article: Short-wavelength Reverberant Wave Systems for Enhanced Reservoir Computing
Short-wavelength Reverberant Wave Systems for Enhanced Reservoir Computing Open
Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-spe…
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics-based numeric…
View article: Machine-Learning-Assisted Hybrid Earth System Modelling
Machine-Learning-Assisted Hybrid Earth System Modelling Open
We propose a novel hybrid modeling approach that combines a physics-based, numerical model of the Earth system with a machine learning model. We also envision to integrate the hybrid model training with a data assimilation cycle, so that n…
View article: Deep learning estimation of complex reverberant wave fields by a programmable metasurface
Deep learning estimation of complex reverberant wave fields by a programmable metasurface Open
Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging, particularly in reverberating environments. T…
View article: Deep Wavefront Shaping: Intelligent Control of Complex Scattering Responses with a Programmable Metasurface
Deep Wavefront Shaping: Intelligent Control of Complex Scattering Responses with a Programmable Metasurface Open
Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging. The use of intelligent, reconfigurable metasu…
View article: Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct them
Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct them Open
Network science is a rapidly expanding field, with a large and growing body of work on network-based dynamical processes. Most theoretical results in this area rely on the so-called locally treelike approximation. This is, however, usually…
View article: A Machine Learning‐Based Global Atmospheric Forecast Model
A Machine Learning‐Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir computing‐based, low‐resolution, global prediction model. The model is designed to take advantage of the massively parallel arc…
View article: A Machine-Learning-Based Global Atmospheric Forecast Model
A Machine-Learning-Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir-computing-based, low-resolution, global prediction model. The model is designed to take advantage of the massively parallel arc…
View article: A Machine-Learning-Based Global Atmospheric Forecast Model
A Machine-Learning-Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a low-resolution ML model for global weather prediction. The forecast performance of the ML model is assessed by comparing it to that of pe…
View article: Dynamic regulation of resource transport induces criticality in interdependent networks of excitable units
Dynamic regulation of resource transport induces criticality in interdependent networks of excitable units Open
Various functions of a network of excitable units can be enhanced if the network is in the "critical regime," where excitations are, on average, neither damped nor amplified. An important question is how can such networks self-organize to …