TY - JOUR A1 - Taghvaei, Amirhossein A1 - de Wiljes, Jana A1 - Mehta, Prashant G. A1 - Reich, Sebastian T1 - Kalman filter and its modern extensions for the continuous-time nonlinear filtering problem JF - Journal of dynamic systems measurement and control N2 - This paper is concerned with the filtering problem in continuous time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman-Bucy filter, which provides an exact solution for the linear Gaussian problem; (ii) the ensemble Kalman-Bucy filter (EnKBF), which is an approximate filter and represents an extension of the Kalman-Bucy filter to nonlinear problems; and (iii) the feedback particle filter (FPF), which represents an extension of the EnKBF and furthermore provides for a consistent solution in the general nonlinear, non-Gaussian case. The common feature of the three algorithms is the gain times error formula to implement the update step (to account for conditioning due to the observations) in the filter. In contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF avoid the resampling of the particles in the importance sampling update step. Moreover, the feedback control structure provides for error correction potentially leading to smaller simulation variance and improved stability properties. The paper also discusses the issue of nonuniqueness of the filter update formula and formulates a novel approximation algorithm based on ideas from optimal transport and coupling of measures. Performance of this and other algorithms is illustrated for a numerical example. Y1 - 2017 U6 - https://doi.org/10.1115/1.4037780 SN - 0022-0434 SN - 1528-9028 VL - 140 IS - 3 PB - ASME CY - New York ER - TY - JOUR A1 - de Wiljes, Jana A1 - Reich, Sebastian A1 - Stannat, Wilhelm T1 - Long-Time stability and accuracy of the ensemble Kalman-Bucy Filter for fully observed processes and small measurement noise JF - SIAM Journal on Applied Dynamical Systems N2 - The ensemble Kalman filter has become a popular data assimilation technique in the geosciences. However, little is known theoretically about its long term stability and accuracy. In this paper, we investigate the behavior of an ensemble Kalman-Bucy filter applied to continuous-time filtering problems. We derive mean field limiting equations as the ensemble size goes to infinity as well as uniform-in-time accuracy and stability results for finite ensemble sizes. The later results require that the process is fully observed and that the measurement noise is small. We also demonstrate that our ensemble Kalman-Bucy filter is consistent with the classic Kalman-Bucy filter for linear systems and Gaussian processes. We finally verify our theoretical findings for the Lorenz-63 system. KW - data assimilation KW - Kalman Bucy filter KW - ensemble Kalman filter KW - stability KW - accuracy KW - asymptotic behavior Y1 - 2018 U6 - https://doi.org/10.1137/17M1119056 SN - 1536-0040 VL - 17 IS - 2 SP - 1152 EP - 1181 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER - TY - JOUR A1 - Hamm, Maximilian A1 - Pelivan, Ivanka A1 - Grott, Matthias A1 - de Wiljes, Jana T1 - Thermophysical modelling and parameter estimation of small solar system bodies via data assimilation JF - Monthly notices of the Royal Astronomical Society N2 - Deriving thermophysical properties such as thermal inertia from thermal infrared observations provides useful insights into the structure of the surface material on planetary bodies. The estimation of these properties is usually done by fitting temperature variations calculated by thermophysical models to infrared observations. For multiple free model parameters, traditional methods such as least-squares fitting or Markov chain Monte Carlo methods become computationally too expensive. Consequently, the simultaneous estimation of several thermophysical parameters, together with their corresponding uncertainties and correlations, is often not computationally feasible and the analysis is usually reduced to fitting one or two parameters. Data assimilation (DA) methods have been shown to be robust while sufficiently accurate and computationally affordable even for a large number of parameters. This paper will introduce a standard sequential DA method, the ensemble square root filter, for thermophysical modelling of asteroid surfaces. This method is used to re-analyse infrared observations of the MARA instrument, which measured the diurnal temperature variation of a single boulder on the surface of near-Earth asteroid (162173) Ryugu. The thermal inertia is estimated to be 295 +/- 18 Jm(-2) K-1 s(-1/2), while all five free parameters of the initial analysis are varied and estimated simultaneously. Based on this thermal inertia estimate the thermal conductivity of the boulder is estimated to be between 0.07 and 0.12,Wm(-1) K-1 and the porosity to be between 0.30 and 0.52. For the first time in thermophysical parameter derivation, correlations and uncertainties of all free model parameters are incorporated in the estimation procedure that is more than 5000 times more efficient than a comparable parameter sweep. KW - radiation mechanisms: thermal KW - methods: data analysis KW - methods KW - statistical KW - minor planets, asteroids: individual: (162173) Ryugu Y1 - 2020 U6 - https://doi.org/10.1093/mnras/staa1755 SN - 0035-8711 SN - 1365-2966 VL - 496 IS - 3 SP - 2776 EP - 2785 PB - Oxford Univ. Press CY - Oxford ER - TY - GEN A1 - Maier, Corinna A1 - Hartung, Niklas A1 - de Wiljes, Jana A1 - Kloft, Charlotte A1 - Huisinga, Wilhelm T1 - Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 827 KW - Induced neutropenia KW - Model KW - Myelosuppression KW - Prediction Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-445500 SN - 1866-8372 IS - 827 ER - TY - JOUR A1 - Maier, Corinna A1 - Hartung, Niklas A1 - de Wiljes, Jana A1 - Kloft, Charlotte A1 - Huisinga, Wilhelm T1 - Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy JF - CPT: Pharmacometrics & Systems Pharmacology N2 - An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas. KW - Induced neutropenia KW - Model KW - Myelosuppression KW - Prediction Y1 - 2019 U6 - https://doi.org/10.1002/psp4.12492 SN - 2163-8306 VL - XX PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Saggioro, Elena A1 - de Wiljes, Jana A1 - Kretschmer, Marlene A1 - Runge, Jakob T1 - Reconstructing regime-dependent causal relationships from observational time series JF - Chaos : an interdisciplinary journal of nonlinear science N2 - Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regime learning optimization approach. PCMCI allows for causal discovery from high-dimensional and highly correlated time series. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, and sign of causal links, as well as changes in the variables' autocorrelation. Furthermore, Regime-PCMCI is employed to observations of El Nino Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets. Y1 - 2020 U6 - https://doi.org/10.1063/5.0020538 SN - 1054-1500 SN - 1089-7682 SN - 1527-2443 VL - 30 IS - 11 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - de Wiljes, Jana A1 - Pathiraja, Sahani Darschika A1 - Reich, Sebastian T1 - Ensemble transform algorithms for nonlinear smoothing problems JF - SIAM journal on scientific computing N2 - Several numerical tools designed to overcome the challenges of smoothing in a non-linear and non-Gaussian setting are investigated for a class of particle smoothers. The considered family of smoothers is induced by the class of linear ensemble transform filters which contains classical filters such as the stochastic ensemble Kalman filter, the ensemble square root filter, and the recently introduced nonlinear ensemble transform filter. Further the ensemble transform particle smoother is introduced and particularly highlighted as it is consistent in the particle limit and does not require assumptions with respect to the family of the posterior distribution. The linear update pattern of the considered class of linear ensemble transform smoothers allows one to implement important supplementary techniques such as adaptive spread corrections, hybrid formulations, and localization in order to facilitate their application to complex estimation problems. These additional features are derived and numerically investigated for a sequence of increasingly challenging test problems. KW - data assimilation KW - smoother KW - localization KW - optimal transport KW - adaptive KW - spread correction Y1 - 2019 U6 - https://doi.org/10.1137/19M1239544 SN - 1064-8275 SN - 1095-7197 VL - 42 IS - 1 SP - A87 EP - A114 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER -