A5010500828- Ecosystem dynamics and resilience
- Model Reduction and Neural Networks
- Complex Systems and Time Series Analysis
- Energy Load and Power Forecasting
- Machine Fault Diagnosis Techniques
- Fluid Dynamics and Vibration Analysis
- Climate Change and Health Impacts
- NMR spectroscopy and applications
- Nonlinear Dynamics and Pattern Formation
- Sustainability and Ecological Systems Analysis
- Animal Ecology and Behavior Studies
- Peer-to-Peer Network Technologies
- Gaussian Processes and Bayesian Inference
- Fault Detection and Control Systems
- Quantum Computing Algorithms and Architecture
- Complex Network Analysis Techniques
- Power System Optimization and Stability
- Probabilistic and Robust Engineering Design
- Quantum Information and Cryptography
- stochastic dynamics and bifurcation
- Reservoir Engineering and Simulation Methods
- Hydrocarbon exploration and reservoir analysis
- Particle Accelerators and Free-Electron Lasers
- Advanced Control Systems Optimization
- Climate variability and models
Carleton University
2025
University of Waterloo
2022-2024
University of Guelph
2024
University of Washington
2017-2022
University of Washington Applied Physics Laboratory
2019
Georgetown University
2016
This report summarises the physics opportunities in search and study of beyond Standard Model at a 100 TeV pp collider.
Abstract. Tipping points characterize the situation when a system experiences abrupt, rapid, and sometimes irreversible changes in response to only gradual change environmental conditions. Given that such events are most cases undesirable, numerous approaches have been proposed identify if is approaching tipping point. Such termed early warning signals represent set of methods for identifying statistical underlying behaviour across time or space would be indicative an Although idea warnings...
The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modelling techniques quite difficult. This has led the development alternative suite methods that seek identify signatures critical phenomena data, are expected occur advance many classes dynamical bifurcation. Crucially, manifestations generic across variety systems, meaning...
Many natural and man-made systems are prone to critical transitions-abrupt potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for transitions by generic features of bifurcations from large simulated training data sets. So far, have only been trained predict continuous-time bifurcations, ignoring rich dynamics unique discrete-time bifurcations. Here, we train a deep classifier the five local codimension-one. We test on simulation models...
We present a data-driven method for separating complex, multiscale systems into their constituent time-scale components using recursive implementation of dynamic mode decomposition (DMD). Local linear models are built from windowed subsets the data, and dominant time scales discovered spectral clustering on eigenvalues. This approach produces series data each identified component, which sum to faithful reconstruction input signal. It differs most other methods in field multiresolution...
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation the Koopman operator, which seeks linear representation observed nonlinear dynamics. Recent work has demonstrated efficacy dynamic mode decomposition (DMD) obtaining finite-dimensional approximations in delay coordinates. In this paper we demonstrate how dynamics with sparse Fourier spectra can be (i) represented by superposition principal component trajectories and (ii) modeled DMD...
<title>Abstract</title> Deep learning models have demonstrated remarkable success in recognising tipping points and providing early warning signals. However, there has been limited exploration of their application to dynamical systems governed by coloured noise, which characterizes many real-world systems. In this study, we that, using the normal form theorem, it is possible leverage forms three primary types bifurcations (Fold, Transcritical, Hopf) construct a training set that enables deep...
Abstract. Tipping points characterize the situation when a system experiences abrupt, rapid and sometimes irreversible changes. Given that such changes are in most cases undesirable, numerous approaches have been proposed to identify if is close tipping point. Such termed early-warning signals represent set of methods for identifying statistical underlying behavior across time or space would be indicative an approaching Although idea early-warnings class not new, last two decades, topic...
Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce novel load method which observed dynamics are modeled as forced linear system using Dynamic Mode Decomposition (DMD) time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an "almost-periodic" character, i.e., continuous Fourier spectrum punctuated by dominant peaks, capture regular (e.g., daily or...
Multiscale phenomena that evolve on multiple distinct timescales are prevalent throughout the sciences. It is often case governing equations of persistent and approximately periodic fast scales prescribed, while emergent slow scale evolution unknown. Yet course-grained, dynamics greatest interest in practice. In this work we present an accurate efficient method for extracting timescale from signals exhibiting amenable to averaging. The relies tracking signal at evenly spaced intervals with...
Theoretical models of spins coupled to bosons provide a simple setting for studying broad range important phenomena in many-body physics, from virtually mediated interactions decoherence and thermalization. In many atomic, molecular, optical systems, such also underlie the most successful attempts engineer strong, long-ranged purpose entanglement generation. Especially when coupling between is that it cannot be treated perturbatively, properties are extremely challenging calculate...
Networked power grid systems are susceptible to a phenomenon known as Coherent Swing Instability (CSI), in which subset of machines the lose synchrony with rest network. We develop network level evaluation metrics (i) identify community substructures network, (ii) determine weak points that particularly sensitive CSI, and (iii) produce an engineering approach for addition transmission lines reduce incidences CSI existing networks, or design new networks robust by their design. For...
Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded much higher state space. In many cases practical relevance, form this embedding is poorly understood or entirely unknown. This paper explores how measurement phenomena that generically precede such bifurcations be used to make...
Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded much higher state space. In many cases practical relevance, form this embedding is poorly understood or entirely unknown. This paper explores how measurement phenomena that generically precede such bifurcations be used to make...
Supplement A -Literature review classificationsWe did a topic search (TS) in the Web of Science for period from 01.01.2004 to 01.04
Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce novel load method which observed dynamics are modeled as forced linear system using Dynamic Mode Decomposition (DMD) time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an "almost-periodic" character, i.e., continuous Fourier spectrum punctuated by dominant peaks, capture regular (e.g., daily or...
Many natural and man-made systems are prone to critical transitions -- abrupt potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal (EWS) for by generic features of bifurcations (dynamical instabilities) from large simulated training data sets. So far, have only been trained predict continuous-time bifurcations, ignoring rich dynamics unique discrete-time bifurcations. Here, we train a deep classifier EWS the five local codimension-1. We...
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation the Koopman operator, which seeks linear representation observed nonlinear dynamics. Recent work has demonstrated efficacy Dynamic Mode Decomposition (DMD) obtaining finite-dimensional approximations in delay coordinates. In this paper we demonstrate how dynamics with sparse Fourier spectra can be (i) represented by superposition principal component trajectories (PCT) and (ii) modeled...