Tag: Model Reduction and Neural Networks
Neural-network surrogate predicts particle hydrodynamic responses accurately
—
in MechanicsWhat the study found A data-driven surrogate framework was developed to estimate hydrodynamic responses of rigid, non-spherical particles in Stokes flow, including the stresslet, angular velocity, and, for helicoidal particles, chiral thrust. The surrogate was trained on data generated by a validated boundary element method and was tested across random orientations and flow types. Why…
Sequential ridge leverage score estimates enable Nystrom KRR
—
What the study found The study introduces INK-ESTIMATE, an algorithm that incrementally computes estimates of ridge leverage scores for large-scale kernel ridge regression (KRR). The authors report that it maintains a small sketch of the kernel matrix, uses a single pass over the matrix, and works with a fixed, small space budget. Why the authors…
Physics-guided machine learning improved waveform prediction under sparse data
—
Physics-guided machine learning framework predicts quasi-isentropic waveforms from sparse data, achieving 96% accuracy and reducing computational resource requirements for material design.
