Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz,R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga,R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. URL https://www.tensorflow.org/. Bano, G., Facco, P., Bezzo, F., and Barolo, M. (2018). Probabilistic design space determination in pharmaceutical product development: A Bayesian/latent variable approach. AIChE Journal, 64, 2438–2449. doi:10.1002/aic.16133. Blankenship, J.W. and Falk, J.E. (1976). Infinitely constrained optimization problems. Journal of Optimization Theory & Applications, 19(2), 261–281. doi:10.1007/BF00934096. Chollet, F. et al. (2015). Keras.https://keras.io. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals & Systems, 2(4), 303–314. doi:10.1007/BF02551274. Garcia-Munoz, S., Luciani, C.V., Vaidyaraman, S., and Seibert, K.D. (2015). Definition of design spaces using mechanistic models and geometric projections of probability maps. Organic Process Research & Development, 19(8), 1012–1023. doi:10.1021/acs.oprd.5b00158. Hart, W.E., Laird, C.D., Watson, J.P., Woodruff, D.L., Hackebeil, G.A., Nicholson, B.L., and Siirola, J.D. (2017). Pyomo – optimization modeling in python, volume 67. Springer Science & Business Media, second edition. Hart, W.E., Watson, J.P., and Woodruff, D.L. (2011). Pyomo: modeling and solving mathematical programs in python. Mathematical Programming Computation, 3(3), 219–260. Hettich, R. and Kortanek, K.O. (1993). Semi-infinite programming: Theory, methods, and applications. SIAM Review, 35(3), 380–429. doi:10.1137/1035089. Holm, P., Alleso, M., Bryder, M.C., and Holm, R. (2017). Q8(R2). In ICH Quality Guidelines, 535–577. John Wiley & Sons, Inc., Hoboken, NJ, USA. doi:10.1002/9781118971147.ch20. Kusumo, K.P., Gomoescu, L., Paulen, R., Garcia Munoz, S., Pantelides, C.C., Shah, N., and Chachuat, B. (2020). Bayesian approach to probabilistic design space characterization: A nested sampling strategy. Industrial & Engineering Chemistry Research, 59(6), 2396–2408. doi:10.1021/acs.iecr.9b05006. Laky, D., Xu, S., Rodriguez, J.S., Vaidyaraman, S., Garcia Munoz, S., and Laird, C. (2019). An optimization-based framework to define the probabilistic design space of pharmaceutical processes with model uncertainty. Processes, 7(2), 96.doi:10.3390/pr7020096. Mitsos, A. (2011). Global optimization of semi-infinite programs via restriction of the right-hand side. Optimization, 60(10-11), 1291–1308. doi:10.1080/02331934.2010.527970. Mukherjee, P., Parkinson, D., and Liddle, A.R. (2006). A nested sampling algorithm for cosmological model selection. The Astro-physical Journal, 638(2), L51–L54. doi:10.1086/501068. Nicholson, B., Siirola, J.D., Watson, J.P., Zavala, V.M., and Biegler,L.T. (2018). Pyomo.Dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations. Mathematical Programming Computation, 10(2), 187–223.doi:10.1007/s12532-017-0127-0. Peterson, J.J. (2008). A Bayesian approach to the ICH Q8 definition of design space. Journal of Biopharmaceutical Statistics, 18(5),959–975. doi:10.1080/10543400802278197. Pulsipher, J.L. and Zavala, V.M. (2019). A scalable stochastic programming approach for the design of flexible systems. Computers & Chemical Engineering, 128, 69–76. doi:10.1016/j.compchemeng.2019.05.033. Samsatli, N., Papageorgiou, L., and Shah, N. (1999). Batch process design and operation using operational envelopes. Computers & Chemical Engineering, 23, S887–S890. doi:10.1016/S0098-1354(99)80218-X. Schweidtmann, A.M. and Mitsos, A. (2019). Deterministic global optimization with artificial neural networks embedded. Journalof Optimization Theory & Applications. doi:10.1007/s10957-018-1396-0. Straub, D.A. and Grossmann, I.E. (1993). Design optimization ofstochastic flexibility. Computers & Chemical Engineering, 17(4),339–354. doi:10.1016/0098-1354(93)80025-I. Tawarmalani, M. and Sahinidis, N.V. (2005). A polyhedral branch-and-cut approach to global optimization. Mathematical Programming, 103, 225–249. doi:10.1007/s10107-005-0581-8.