The phrase "develop robustly" is correct and usable in written English.
This phrase can be used to describe a situation wherein an entity (like a company, organization, project, etc) is developing at a strong and efficient pace. For example, "The new project was developing robustly, thanks to the team's hard work."
From a common ground that included a taste for popular culture, figuration and a certain tactility, as well a sly alertness to Abstract Expressionism and Surrealism, they developed robustly distinct sensibilities that are represented here mostly by excellent, little-seen examples.
The standardized automated procedure that was developed robustly determined regional [C]PIB retention, demonstrating its feasibility for efficient image processing in multicentre studies.
The main purpose of this study was to investigate the distribution of Puroindoline-D1 alleles in landraces and introduced cultivars of the Yellow and Huai Valley of China, in order to further characterize the molecular mechanism of PINA-null alleles, and to develop a robustly PCR-based molecular marker approach for wheat breeding.
"I did watch, and occasionally unscrewed a spark plug," he recalled, but the expertise he developed most robustly was "ferrying in cherry Cokes from the nearby restaurant".
A number of codes are developed to robustly perform estimation of helicopter configuration from sizing, performance analysis, trim analysis, to rotor blades configuration representation.
First, second-order sliding mode guidance (SOSMG) law is developed to robustly follow the pre-designed nominal trajectory under larger uncertainties and effectively reduce the longitudinal error.
Starting from this set of pathways, a growth factor cocktail was developed that robustly enhanced osteogenesis in vitro and in vivo.
Different methods have been developed to robustly maintain the background model in scenes with possible changes in background such as gradual change in lighting and sudden changes in illumination due to light switches [8, 9].
A new residual-consensus scheme within which the uncertainty of epipolar transfer is analytically characterized by deriving its closed-form covariance is developed to robustly estimate the noise scale.
The ML-RANSAC algorithm is developed to robustly estimate velocity and position of the multiple moving objects in an unknown environment whereas the state of the objects (static or dynamic) is not known a priori.
