Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
method 1
citation-polarity summary
verdicts
UNVERDICTED 5representative citing papers
EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
A component-based reduced-order modeling framework decomposes multi-injector rocket combustors into trainable sub-models that couple to predict combustion dynamics across flow and geometry changes.
Interpolation-based ROM techniques with Q-DEIM hyper-reduction are applied to reduce computational cost and memory use of stochastic integrals in the SFV method for high-dimensional stochastic spaces.
citing papers explorer
-
Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
-
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
-
A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
-
Component-Based Reduced-Order Modeling Framework for Rocket Combustion Dynamics in Multi-Injector Configurations
A component-based reduced-order modeling framework decomposes multi-injector rocket combustors into trainable sub-models that couple to predict combustion dynamics across flow and geometry changes.
-
Model Order Reduction Techniques for the Stochastic Finite Volume Method
Interpolation-based ROM techniques with Q-DEIM hyper-reduction are applied to reduce computational cost and memory use of stochastic integrals in the SFV method for high-dimensional stochastic spaces.