Tag: Inverse Problems

Researchers have extended a bias-aware calibration framework to multi-physics offshore wind turbine models, successfully identifying structural and aerodynamic parameters from ...

The open-source VIPR framework standardizes machine-learning inference for ill-posed inverse problems in reflectometry and beyond through configurable, reproducible workflows.

A new differential strain method identifies both prestress forces and moving vehicle loads in prestressed concrete bridges using only two ...

Researchers have developed an importance-sampling-based black box variational inference algorithm that reuses earlier simulation evaluations to cut the computational cost ...

A physics-informed neural network framework reconstructs landslide-induced loads on buried pipelines from sparse monitoring data with errors below 0.2% in ...

A new method called physics-embedded inverse learning trains neural networks to estimate unobservable physical parameters by supervising them with final ...

A new conditioning-controlled retrieval framework jointly derives broadband land surface temperature and emissivity from paired ground-based longwave irradiance measurements, achieving ...

In the rapidly evolving field of computational imaging, a groundbreaking advancement has emerged from the collaborative research team led by ...