2015
Manifold Learning for Latent Variable Inference in Dynamical Systems
Talmon R, Mallat S, Zaveri H, Coifman R. Manifold Learning for Latent Variable Inference in Dynamical Systems. IEEE Transactions On Signal Processing 2015, 63: 3843-3856. DOI: 10.1109/tsp.2015.2432731.Peer-Reviewed Original ResearchDynamical systemsLatent variable inferenceOutput signal measurementsNonlinear observerEigenvector problemLaplace operatorSignal geometryIntrinsic distanceSignal measurementsAccurate recoveryIntrinsic variablesLatent variablesObserverInferenceMeasurement deviceManifoldOperatorsVariablesGeometryIntracranial electroencephalography signalsKernelDynamicsPropertiesProblemSolution
2013
Nonlinear intrinsic variables and state reconstruction in multiscale simulations
Dsilva CJ, Talmon R, Rabin N, Coifman RR, Kevrekidis IG. Nonlinear intrinsic variables and state reconstruction in multiscale simulations. The Journal Of Chemical Physics 2013, 139: 184109. PMID: 24320256, DOI: 10.1063/1.4828457.Peer-Reviewed Original ResearchHigh-dimensional simulation dataLow-dimensional descriptionSimulation dataEnzyme reaction networksSlow time scaleStochastic simulationMeasurement noisePartial observationsPhysical phenomenaMultiscale simulationsReaction networksIntrinsic variablesSimulation ensemblesAlanine dipeptideSimulationsState reconstructionEnsembleProcess variabilityTime scalesAtomistic simulationsVariablesNoiseApproachSimple features