Multifidelity optimization combines inexpensive, approximate data with results from limited numbers of high-quality experiments by modifying a well-known data-optimization technique. The result is a new way to inform process development and reduce the number of experiments performed. The authors from the ARC Digital Bioprocess Development Hub share a case study in which simulated and physical data from a 250-mL antibody process were used to improve process productivity.
Multifidelity optimization combines inexpensive, approximate data with results from limited numbers of high-quality experiments by modifying a well-known data-optimization technique. The result is a new way to inform process development and reduce the number of experiments performed. The authors from the ARC Digital Bioprocess Development Hub share a case study in which simulated and physical data from a 250-mL antibody process were used to improve process productivity.