Argonne Captures 3 R&D 100 Awards for Innovative Technology – HPCwire

Nov. 5, 2021 — Three technologies developed by researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and partner organizations have been named as 2021 R&100 Award winners, building on a decades-long history of wins.

Argonne’s R&D 100 winnersR&D 100 Winners (top left to bottom right): Franck Cappello, Sheng Di, Pinaki Pal, Robert Ross, Philip Carns, Sibendu Som. (Image by Argonne National Laboratory.)

Recognizing the 100 most innovative technologies of the past year, the R&100 Awards are considered the ​“Oscars” of innovation. Sponsored by R&D World magazine, the renowned worldwide competition received entries from 17 countries/regions.

Started in 1963, the R&100 Awards serve as the nation’s most prestigious innovation awards program, honoring R&D pioneers and their revolutionary ideas in science and technology. Technologies are chosen from three categories: Mechanical/Materials, Process/Prototyping and Software/Services.

Argonne scientists have received more than 130 R&100 Awards since the competition began. Past winners include Fortune 500 companies, DOE national laboratories, academic institutions and smaller companies.

The Argonne technologies described below were selected as winners by an independent panel of more than 40 industry leaders. Winners were recognized at the virtual 2021 R&100 Conference on Oct. 2122.

ML-GA: a Machine Learning-Genetic Algorithm for Rapid Product Design Optimization

Pinaki Pal, Opeoluwa Owoyele, Ahmed Abdul Moiz, Janardhan Kodavasal, Sibendu Som

Machine Learning — Genetic Algorithm (ML-GA) is a unique software technology that harnesses the power of advanced machine learning to speed up the virtual design of products and manufacturing processes across a wide range of industries.

Industrial product design involves a large number of control parameters that are often time-consuming and costly to optimize, even with computer modeling. By embedding ML into the design process, ML-GA dramatically speeds up computer-aided engineering simulation-driven virtual prototyping, dramatically shrinking the product …….


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