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From the 1 of 9 linked papers with an AI index.

activity
20242026
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9 papers

physics.flu-dyn2026

Laminar gaps mirror turbulent puffs in pipe flow

Shai Kapon, Tobias Grafke, Anna Frishman

Pipe flow at intermediate Reynolds numbers, between the laminar and fully turbulent regimes, takes the form of several spatially and temporally intermittent phases in which turbule…

cond-mat.stat-mech2026

Escape over a saddle by coloured noise: theory and numerics

Jiayao Shao, Tobias Grafke, Robert S. MacKay

The paper develops new computational methods to study rare escape events over a saddle point in stochastic systems driven by coloured or degenerate noise, introducing a Hamiltonian…

stat.CO2026

Scalability of the second-order reliability method for stochastic differential equations with multiplicative noise

Timo Schorlepp, Tobias Grafke

We show how to efficiently compute asymptotically sharp estimates of extreme event probabilities in stochastic differential equations (SDEs) with small multiplicative Brownian nois…

math.NA2026

Exponential time differencing for matrix-valued dynamical systems

Nayef Shkeir, Tobias Grafke

Matrix evolution equations occur in many applications, such as dynamical Lyapunov/Sylvester systems or Riccati equations in optimization and stochastic control, machine learning or…

physics.flu-dyn2025

Self-Replication of Turbulent Puffs: On the edge between chaotic saddles

Anton Svirsky, Tobias Grafke, Anna Frishman

Pipe flow is a canonical example where turbulence first appears intermittently in space and time, taking the form of localized structures termed puffs. Turbulence spreads via puff…

stat.ML2025

Sampling conditioned diffusions via Pathspace Projected Monte Carlo

Tobias Grafke

We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic…