Results 201 to 210 of about 935 (254)

On MAP Estimates and Source Conditions for Drift Identification in SDEs

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT We consider the inverse problem of identifying the drift in an stochastic differential equation (SDE) from n$n$ observations of its solution at M+1$M+1$ distinct time points. We derive a corresponding maximum a posteriori (MAP) estimate, we prove differentiability properties as well as a so‐called tangential cone condition for the forward ...
Daniel Tenbrinck   +3 more
wiley   +1 more source

Simultaneous Inversion for Underactuated Mechanical Systems with Servo‐Constraints

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT The dynamic inversion of underactuated mechanical systems can be formulated in the servo‐constraint framework using a set of differential‐algebraic equations (DAEs). In case of a high differentiation index, the inversion‐based feedforward control design poses significant challenges.
Tengman Wang
wiley   +1 more source

Iterative Krylov Subspace Methods for Linear Port‐Hamiltonian Systems

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT In this work, we present a structure‐preserving Krylov subspace iteration scheme for solving the equation systems that arise from the Gauss integration of linear energy‐conserving and dissipative differential systems (e.g., Poisson systems, gradient systems, and port‐Hamiltonian systems).
Stefan Maier, Nicole Marheineke
wiley   +1 more source

Quantum Error Mitigation in Optimized Circuits for Particle-Density Correlations in Real-Time Dynamics of the Schwinger Model. [PDF]

open access: yesEntropy (Basel)
Pomarico D   +7 more
europepmc   +1 more source

Toward an Efficient Shifted Cholesky QR for Applications in Model Order Reduction Using pyMOR

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT Many model order reduction (MOR) methods rely on the computation of an orthonormal basis of a subspace onto which the large full order model is projected. Numerically, this entails the orthogonalization of a set of vectors. The nature of the MOR process imposes several requirements for the orthogonalization process.
Maximilian Bindhak   +2 more
wiley   +1 more source

Efficient quantum thermal simulation. [PDF]

open access: yesNature
Chen CF   +3 more
europepmc   +1 more source

Home - About - Disclaimer - Privacy