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Case Study
Case Study
robust Transfer Learning BO (rTLBO)
robust Transfer Learning BO (rTLBO)
Merck Germany extensively uses TLBO in their BayBE software to efficiently optimise chemical reactions and cell lines, see below.
rTLBO is an advanced version of TLBO that automatically ignores unreliable data when learning from relevant but not identical historical data.
Robustly learning from historical data USD, depending on your use of Bayesian Optimisation and historical data.
Business Impact
Business Impact
We project that this method has the potential to drastically reduce experimental costs (depending on the level of unreliability in the historical data). Its commercial value depends on the expected number of faulty reactors and the severity of unreliable data measured from those reactors.
Realistically, we conservatively estimate the average improvement (of using rTLBO instead of TLBO) to be between 5% and 25%. Ultimately, the business value of rTLBO will differ depending on our client's individual circumstances.
As we don't believe in making big blanket statements, thus, to better understand its value for your operations, please use the commercial value calculator below.
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