@misc{Reich1995, author = {Reich, Sebastian}, title = {Smoothed dynamics of highly oscillatory Hamiltonian systems}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-15639}, year = {1995}, abstract = {We consider the numerical treatment of Hamiltonian systems that contain a potential which grows large when the system deviates from the equilibrium value of the potential. Such systems arise, e.g., in molecular dynamics simulations and the spatial discretization of Hamiltonian partial differential equations. Since the presence of highly oscillatory terms in the solutions forces any explicit integrator to use very small step size, the numerical integration of such systems provides a challenging task. It has been suggested before to replace the strong potential by a holonomic constraint that forces the solutions to stay at the equilibrium value of the potential. This approach has, e.g., been successfully applied to the bond stretching in molecular dynamics simulations. In other cases, such as the bond-angle bending, this methods fails due to the introduced rigidity. Here we give a careful analysis of the analytical problem by means of a smoothing operator. This will lead us to the notion of the smoothed dynamics of a highly oscillatory Hamiltonian system. Based on our analysis, we suggest a new constrained formulation that maintains the flexibility of the system while at the same time suppressing the high-frequency components in the solutions and thus allowing for larger time steps. The new constrained formulation is Hamiltonian and can be discretized by the well-known SHAKE method.}, language = {en} } @misc{LeimkuhlerReich1994, author = {Leimkuhler, Benedict and Reich, Sebastian}, title = {Symplectic integration of constrained Hamiltonian systems}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-15653}, year = {1994}, abstract = {A Hamiltonian system in potential form (formula in the original abstract) subject to smooth constraints on q can be viewed as a Hamiltonian system on a manifold, but numerical computations must be performed in Rn. In this paper methods which reduce "Hamiltonian differential algebraic equations" to ODEs in Euclidean space are examined. The authors study the construction of canonical parameterizations or local charts as well as methods based on the construction of ODE systems in the space in which the constraint manifold is embedded which preserve the constraint manifold as an invariant manifold. In each case, a Hamiltonian system of ordinary differential equations is produced. The stability of the constraint invariants and the behavior of the original Hamiltonian along solutions are investigated both numerically and analytically.}, language = {en} } @misc{AscherChinPetzoldetal.1994, author = {Ascher, Uri M. and Chin, Hongsheng and Petzold, Linda R. and Reich, Sebastian}, title = {Stabilization of constrained mechanical systems with DAEs and invariant manifolds}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-15698}, year = {1994}, abstract = {Many methods have been proposed for the simulation of constrained mechanical systems. The most obvious of these have mild instabilities and drift problems. Consequently, stabilization techniques have been proposed A popular stabilization method is Baumgarte's technique, but the choice of parameters to make it robust has been unclear in practice. Some of the simulation methods that have been proposed and used in computations are reviewed here, from a stability point of view. This involves concepts of differential-algebraic equation (DAE) and ordinary differential equation (ODE) invariants. An explanation of the difficulties that may be encountered using Baumgarte's method is given, and a discussion of why a further quest for better parameter values for this method will always remain frustrating is presented. It is then shown how Baumgarte's method can be improved. An efficient stabilization technique is proposed, which may employ explicit ODE solvers in case of nonstiff or highly oscillatory problems and which relates to coordinate projection methods. Examples of a two-link planar robotic arm and a squeezing mechanism illustrate the effectiveness of this new stabilization method.}, language = {en} } @misc{Reich1980, author = {Reich, Sebastian}, title = {Algebrodifferentialgleichungen und Vektorfelder auf Mannigfaltigkeiten}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-47290}, year = {1980}, abstract = {In diesem Beitrag wird der Zusammenhang zwischen Algebrodifferentialgleichungen (ADGL) und Vektorfeldern auf Mannigfaltigkeiten untersucht. Dazu wird zun{\"a}chst der Begriff der regul{\"a}ren ADGL eingef{\"u}hrt, wobei unter eirter regul{\"a}ren ADGL eine ADGL verstanden wird, deren L{\"o}sungsmenge identisch mit der L{\"o}sungsmenge eines Vektorfeldes ist. Ausgehend von bekannten Aussagen {\"u}ber die L{\"o}sungsmenge eines Vektorfeldes werden analoge Aussagen f{\"u}r die L{\"o}sungsmenge einer regul{\"a}ren ADGL abgeleitet. Es wird eine Reduktionsmethode angegeben, die auf ein Kriterium f{\"u}r die Begularit{\"a}t einer ADGL und auf die Definition des Index einer nichtlinearen ADGL f{\"u}hrt. Außerdem wird gezeigt, daß beliebige Vektorfelder durch regul{\"a}re ADGL so realisiert werden k{\"o}nnen, daß die L{\"o}sungsmenge des Vektorfeldes mit der der realisierenden ADGL identisch ist. Abschließend werden die f{\"u}r autonome ADGL gewonnenen Aussagen auf den Fall der nichtautonomen ADGL {\"u}bertragen.}, language = {de} } @misc{Reich1992, author = {Reich, Sebastian}, title = {Differential-algebraic equations and applications in circuit theory}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-46646}, year = {1992}, abstract = {Technical and physical systems, especially electronic circuits, are frequently modeled as a system of differential and nonlinear implicit equations. In the literature such systems of equations are called differentialalgebraic equations (DAEs). It turns out that the numerical and analytical properties of a DAE depend on an integer called the index of the problem. For example, the well-known BDF method of Gear can be applied, in general, to a DAE only if the index does not exceed one. In this paper we give a geometric interpretation of higherindex DAEs and indicate problems arising in connection with such DAEs by means of several examples.}, language = {en} } @misc{vanLeeuwenKunschNergeretal.2019, author = {van Leeuwen, Peter Jan and Kunsch, Hans R. and Nerger, Lars and Potthast, Roland and Reich, Sebastian}, title = {Particle filters for high-dimensional geoscience applications: A review}, series = {Quarterly journal of the Royal Meteorological Society}, volume = {145}, journal = {Quarterly journal of the Royal Meteorological Society}, number = {723}, publisher = {Wiley}, address = {Hoboken}, issn = {0035-9009}, doi = {10.1002/qj.3551}, pages = {2335 -- 2365}, year = {2019}, abstract = {Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the geosciences, but their application to high-dimensional geoscience systems has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localization and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state-of-the-art discussion of present efforts of developing particle filters for high-dimensional nonlinear geoscience state-estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo-code, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction, suggesting that they will become mainstream soon.}, language = {en} } @book{VanLeeuwenChengReich2015, author = {Van Leeuwen, Peter Jan and Cheng, Yuan and Reich, Sebastian}, title = {Nonlinear data assimilation}, series = {Frontiers in applied dynamical systems: reviews and tutorials ; 2}, journal = {Frontiers in applied dynamical systems: reviews and tutorials ; 2}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-18346-6}, doi = {10.1007/978-3-319-18347-3}, pages = {xii, 118}, year = {2015}, abstract = {This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.}, language = {en} } @article{Reich2019, author = {Reich, Sebastian}, title = {Data assimilation}, series = {Acta numerica}, volume = {28}, journal = {Acta numerica}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {0962-4929}, doi = {10.1017/S0962492919000011}, pages = {635 -- 711}, year = {2019}, abstract = {Data assimilation addresses the general problem of how to combine model-based predictions with partial and noisy observations of the process in an optimal manner. This survey focuses on sequential data assimilation techniques using probabilistic particle-based algorithms. In addition to surveying recent developments for discrete- and continuous-time data assimilation, both in terms of mathematical foundations and algorithmic implementations, we also provide a unifying framework from the perspective of coupling of measures, and Schr{\"o}dinger's boundary value problem for stochastic processes in particular.}, language = {en} } @article{NueskenReichRozdeba2019, author = {N{\"u}sken, Nikolas and Reich, Sebastian and Rozdeba, Paul J.}, title = {State and parameter estimation from observed signal increments}, series = {Entropy : an international and interdisciplinary journal of entropy and information studies}, volume = {21}, journal = {Entropy : an international and interdisciplinary journal of entropy and information studies}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {1099-4300}, doi = {10.3390/e21050505}, pages = {23}, year = {2019}, abstract = {The success of the ensemble Kalman filter has triggered a strong interest in expanding its scope beyond classical state estimation problems. In this paper, we focus on continuous-time data assimilation where the model and measurement errors are correlated and both states and parameters need to be identified. Such scenarios arise from noisy and partial observations of Lagrangian particles which move under a stochastic velocity field involving unknown parameters. We take an appropriate class of McKean-Vlasov equations as the starting point to derive ensemble Kalman-Bucy filter algorithms for combined state and parameter estimation. We demonstrate their performance through a series of increasingly complex multi-scale model systems.}, language = {en} } @article{GarbunoInigoNueskenReich2020, author = {Garbuno-Inigo, Alfredo and N{\"u}sken, Nikolas and Reich, Sebastian}, title = {Affine invariant interacting Langevin dynamics for Bayesian inference}, series = {SIAM journal on applied dynamical systems}, volume = {19}, journal = {SIAM journal on applied dynamical systems}, number = {3}, publisher = {Society for Industrial and Applied Mathematics}, address = {Philadelphia}, issn = {1536-0040}, doi = {10.1137/19M1304891}, pages = {1633 -- 1658}, year = {2020}, abstract = {We propose a computational method (with acronym ALDI) for sampling from a given target distribution based on first-order (overdamped) Langevin dynamics which satisfies the property of affine invariance. The central idea of ALDI is to run an ensemble of particles with their empirical covariance serving as a preconditioner for their underlying Langevin dynamics. ALDI does not require taking the inverse or square root of the empirical covariance matrix, which enables application to high-dimensional sampling problems. The theoretical properties of ALDI are studied in terms of nondegeneracy and ergodicity. Furthermore, we study its connections to diffusion on Riemannian manifolds and Wasserstein gradient flows. Bayesian inference serves as a main application area for ALDI. In case of a forward problem with additive Gaussian measurement errors, ALDI allows for a gradient-free approximation in the spirit of the ensemble Kalman filter. A computational comparison between gradient-free and gradient-based ALDI is provided for a PDE constrained Bayesian inverse problem.}, language = {en} } @article{MaoutsaReichOpper2020, author = {Maoutsa, Dimitra and Reich, Sebastian and Opper, Manfred}, title = {Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation}, series = {Entropy}, volume = {22}, journal = {Entropy}, number = {8}, publisher = {MDPI}, address = {Basel}, issn = {1099-4300}, doi = {10.3390/e22080802}, pages = {35}, year = {2020}, abstract = {Fokker-Planck equations are extensively employed in various scientific fields as they characterise the behaviour of stochastic systems at the level of probability density functions. Although broadly used, they allow for analytical treatment only in limited settings, and often it is inevitable to resort to numerical solutions. Here, we develop a computational approach for simulating the time evolution of Fokker-Planck solutions in terms of a mean field limit of an interacting particle system. The interactions between particles are determined by the gradient of the logarithm of the particle density, approximated here by a novel statistical estimator. The performance of our method shows promising results, with more accurate and less fluctuating statistics compared to direct stochastic simulations of comparable particle number. Taken together, our framework allows for effortless and reliable particle-based simulations of Fokker-Planck equations in low and moderate dimensions. The proposed gradient-log-density estimator is also of independent interest, for example, in the context of optimal control.}, language = {en} } @misc{Reich1990, author = {Reich, Sebastian}, title = {On a geometrical interpretation of differential-algebraic equations}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-46683}, year = {1990}, abstract = {The subject of this paper is the relation of differential-algebraic equations (DAEs) to vector fields on manifolds. For that reason, we introduce the notion of a regular DAE as a DAE to which a vector field uniquely corresponds. Furthermore, a technique is described which yields a family of manifolds for a given DAE. This socalled family of constraint manifolds allows in turn the formulation of sufficient conditions for the regularity of a DAE. and the definition of the index of a regular DAE. We also state a method for the reduction of higher-index DAEs to lowsr-index ones that can be solved without introducing additional constants of integration. Finally, the notion of realizability of a given vector field by a regular DAE is introduced, and it is shown that any vector field can be realized by a regular DAE. Throughout this paper the problem of path-tracing is discussed as an illustration of the mathematical phenomena.}, language = {en} } @misc{Reich1994, author = {Reich, Sebastian}, title = {Momentum conserving symplectic integrators}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-16824}, year = {1994}, abstract = {In this paper, we show that symplectic partitioned Runge-Kutta methods conserve momentum maps corresponding to linear symmetry groups acting on the phase space of Hamiltonian differential equations by extended point transformation. We also generalize this result to constrained systems and show how this conservation property relates to the symplectic integration of Lie-Poisson systems on certain submanifolds of the general matrix group GL(n).}, language = {en} } @misc{Reich1995, author = {Reich, Sebastian}, title = {On the local qualitative behavior of differential-algebraic equations}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-46739}, year = {1995}, abstract = {A theoretical famework for the investigation of the qualitative behavior of differential-algebraic equations (DAEs) near an equilibrium point is established. The key notion of our approach is the notion of regularity. A DAE is called regular locally around an equilibrium point if there is a unique vector field such that the solutions of the DAE and the vector field are in one-to-one correspondence in a neighborhood of this equili Drium point. Sufficient conditions for the regularity of an equilibrium point are stated. This in turn allows us to translate several local results, as formulated for vector fields, to DAEs that are regular locally around a g: ven equilibrium point (e.g. Local Stable and Unstable Manifold Theorem, Hopf theorem). It is important that ihese theorems are stated in terms of the given problem and not in terms of the corresponding vector field.}, language = {en} } @misc{Reich1991, author = {Reich, Sebastian}, title = {On an existence and uniqueness theory for nonlinear differential-algebraic equations}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-46706}, year = {1991}, abstract = {An existence and uniqueness theory is developed for general nonlinear and nonautonomous differential-algebraic equations (DAEs) by exploiting their underlying differential-geometric structure. A DAE is called regular if there is a unique nonautonomous vector field such that the solutions of the DAE and the solutions of the vector field are in one-to-one correspondence. Sufficient conditions for regularity of a DAE are derived in terms of constrained manifolds. Based on this differential-geometric characterization, existence and uniqueness results are stated for regular DAEs. Furthermore, our not ons are compared with techniques frequently used in the literature such as index and solvability. The results are illustrated in detail by means of a simple circuit example.}, language = {en} } @article{GottwaldReich2021, author = {Gottwald, Georg A. and Reich, Sebastian}, title = {Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations}, series = {Chaos : an interdisciplinary journal of nonlinear science}, volume = {31}, journal = {Chaos : an interdisciplinary journal of nonlinear science}, number = {10}, publisher = {AIP}, address = {Melville}, issn = {1054-1500}, doi = {10.1063/5.0066080}, pages = {8}, year = {2021}, abstract = {We present a supervised learning method to learn the propagator map of a dynamical system from partial and noisy observations. In our computationally cheap and easy-to-implement framework, a neural network consisting of random feature maps is trained sequentially by incoming observations within a data assimilation procedure. By employing Takens's embedding theorem, the network is trained on delay coordinates. We show that the combination of random feature maps and data assimilation, called RAFDA, outperforms standard random feature maps for which the dynamics is learned using batch data.}, language = {en} } @article{GottwaldMitchellReich2011, author = {Gottwald, Georg A. and Mitchell, Lewis and Reich, Sebastian}, title = {Controlling overestimation of error covariance in ensemble kalman filters with sparse observations a variance-limiting kalman filter}, series = {Monthly weather review}, volume = {139}, journal = {Monthly weather review}, number = {8}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0027-0644}, doi = {10.1175/2011MWR3557.1}, pages = {2650 -- 2667}, year = {2011}, abstract = {The problem of an ensemble Kalman filter when only partial observations are available is considered. In particular, the situation is investigated where the observational space consists of variables that are directly observable with known observational error, and of variables of which only their climatic variance and mean are given. To limit the variance of the latter poorly resolved variables a variance-limiting Kalman filter (VLKF) is derived in a variational setting. The VLKF for a simple linear toy model is analyzed and its range of optimal performance is determined. The VLKF is explored in an ensemble transform setting for the Lorenz-96 system, and it is shown that incorporating the information of the variance of some unobservable variables can improve the skill and also increase the stability of the data assimilation procedure.}, language = {en} } @article{GottwaldReich2021, author = {Gottwald, Georg A. and Reich, Sebastian}, title = {Supervised learning from noisy observations}, series = {Physica : D, Nonlinear phenomena}, volume = {423}, journal = {Physica : D, Nonlinear phenomena}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-2789}, doi = {10.1016/j.physd.2021.132911}, pages = {15}, year = {2021}, abstract = {Data-driven prediction and physics-agnostic machine-learning methods have attracted increased interest in recent years achieving forecast horizons going well beyond those to be expected for chaotic dynamical systems. In a separate strand of research data-assimilation has been successfully used to optimally combine forecast models and their inherent uncertainty with incoming noisy observations. The key idea in our work here is to achieve increased forecast capabilities by judiciously combining machine-learning algorithms and data assimilation. We combine the physics-agnostic data -driven approach of random feature maps as a forecast model within an ensemble Kalman filter data assimilation procedure. The machine-learning model is learned sequentially by incorporating incoming noisy observations. We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained. Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems. (C) 2021 Elsevier B.V. All rights reserved.}, language = {en} } @article{EngbertRabeSchwetlicketal.2022, author = {Engbert, Ralf and Rabe, Maximilian Michael and Schwetlick, Lisa and Seelig, Stefan A. and Reich, Sebastian and Vasishth, Shravan}, title = {Data assimilation in dynamical cognitive science}, series = {Trends in cognitive sciences}, volume = {26}, journal = {Trends in cognitive sciences}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1364-6613}, doi = {10.1016/j.tics.2021.11.006}, pages = {99 -- 102}, year = {2022}, abstract = {Dynamical models make specific assumptions about cognitive processes that generate human behavior. In data assimilation, these models are tested against timeordered data. Recent progress on Bayesian data assimilation demonstrates that this approach combines the strengths of statistical modeling of individual differences with the those of dynamical cognitive models.}, language = {en} } @article{ReichWeissmann2021, author = {Reich, Sebastian and Weissmann, Simon}, title = {Fokker-Planck particle systems for Bayesian inference: computational approaches}, series = {SIAM ASA journal on uncertainty quantification}, volume = {9}, journal = {SIAM ASA journal on uncertainty quantification}, number = {2}, publisher = {Society for Industrial and Applied Mathematics}, address = {Philadelphia}, issn = {2166-2525}, doi = {10.1137/19M1303162}, pages = {446 -- 482}, year = {2021}, abstract = {Bayesian inference can be embedded into an appropriately defined dynamics in the space of probability measures. In this paper, we take Brownian motion and its associated Fokker-Planck equation as a starting point for such embeddings and explore several interacting particle approximations. More specifically, we consider both deterministic and stochastic interacting particle systems and combine them with the idea of preconditioning by the empirical covariance matrix. In addition to leading to affine invariant formulations which asymptotically speed up convergence, preconditioning allows for gradient-free implementations in the spirit of the ensemble Kalman filter. While such gradient-free implementations have been demonstrated to work well for posterior measures that are nearly Gaussian, we extend their scope of applicability to multimodal measures by introducing localized gradient-free approximations. Numerical results demonstrate the effectiveness of the considered methodologies.}, language = {en} }