1 citations · 1 across the 3 of their papers we have counts for
4 papers
A Memory-Efficient Adjoint State Optimization Method Based on Time-Reversible Dynamical Low-Rank Approximation
Lukas Einkemmer, Julian Mangott
The primary challenge of conducting PDE-constrained optimization for high-dimensional problems, such as kinetic equations, is the often prohibitive memory cost. Computing gradients…
Automatic partitioning for the low-rank integration of stochastic Boolean reaction networks
Lukas Einkemmer, Julian Mangott, Martina Prugger
Boolean reaction networks are an important tool in biochemistry for studying mechanisms in the biological cell. However, the stochastic formulation of such networks requires the so…
A hierarchical dynamical low-rank algorithm for the stochastic description of large reaction networks
Lukas Einkemmer, Julian Mangott, Martina Prugger
The stochastic description of chemical reaction networks with the kinetic chemical master equation (CME) is important for studying biological cells, but it suffers from the curse o…
A low-rank complexity reduction algorithm for the high-dimensional kinetic chemical master equation
Lukas Einkemmer, Julian Mangott, Martina Prugger
It is increasingly realized that taking stochastic effects into account is important in order to study biological cells. However, the corresponding mathematical formulation, the ch…