Bikmukhametov, T. and Jäschke, J. (2020). Combining
machine learning and process engineering physics towards
enhanced accuracy and explainability of data-driven
models. Comp. and Chem. Eng., 138.

Bottou, L., Curtis, F.E., and Nocedal, J. (2018). Optimization
methods for large-scale machine learning. Society
for Industrial and Applied Mathematics, 60(2), 223-311.

Brastein, O.M., Sharma, R., and Skeie, N.O. (2019).
Sensor placement and parameter identifiability in grey-box
models of building thermal behaviour. Proc. of The
60th SIMS Conf. on Sim. and Modelling, 170, 51-58.

Brun, R., Reichert, P., and Künsch, H. (2001). Practical
identifiability analysis of large environmental simulation
models. Water Resources Research, 37(4), 1015-1030.

Chan, T.F. and Hansen, P. (1992). Some applications of
the rank revealing qr factorization. SIAM Journal on
Scientific and Statistical Computing, 13(3), 727-741.

Deconinck, A.H. and Roels, S. (2017). Is stochastic grey-box
modelling suited for physical properties estimation
of building components from on-site measurements?
Journal of Building Physics, 40(5), 444-471.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep
Learning. The MIT Press, Cambridge, Massachusetts.
London, England.

Grimstad, B., Gunnerud, V., Sandnes, A., Shamlou, S.,
Skrondal, I.S., Uglane, V., Ursin-Holm, S., and Foss, B.
(2016). A Simple Data-Driven Approach to Production
Estimation and Optimization. In: SPE Intelligent Energy Int. Conf. and Exh.

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The
Elements of Statistical Learning. Springer, New York.

He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving
Deep into Rectifiers: Surpassing Human-Level Performance
on ImageNet Classification. In Proc. of the IEEE
int. conf. on computer vision, 1026-1034.

Hotvedt, M., Grimstad, B., and Imsland, L. (2020). Developing
a hybrid data-driven, mechanistic virtual flow meter - a case study. 
arXiv:2002.02737v2.

Jansen, J.D. (2015). Nodal Analysis of Oil and Gas Wells -
Theory and Numerical Implementation. Delft University
of Technology, TU Delft, The Netherlands.

Kingma, D. and Ba, J.L. (2015). Adam: a method
for stochastic optimization. Int. Conf. on learning representations.

Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter,
F. (2017). Fast bayesian optimization of machine learning
hyperparameters on large datasets. In: Proceedings
of the 20th Int. Conf. on AISTATS, 54.

Kramer, M.A., Thomsom, M.L., and Bhagat, P.M. (1992).
Embedding theoretical models in neural networks. Proceedings of ACC, 1, 475-479.

Lundin Energy Norway (2019). Norway - Edvard Grieg.
https://www.lundin-petroleum.com.

Psichogios, D. and Ungar, L. (1992). A hybrid neural
network-first principles approach to process modeling.
AIChE Journal, 38(10), 1499-1511.

Raue, A., Kreutz, C., Maiwald, T., Bachmann, J.,
Schilling, M., Klingmuller, U., and Timmer, J. (2009).
Structural and practical identifiability analysis of partially
observed dynamical models by exploiting the profile likelihood. 
Bioinfromatics, 25 (15), 1923-1929.

Roscher, R., Bohn, B., Duarte, M., and Garcke, J. (2020).
Explainable machine learning for scientific insights and
discoveries. arXiv:1905.08883v3, 1-29.

Sachdeva, R., Schmidt, Z., Brill, J.P., and Blais, R. (1986).
Two-phase flow through chokes. SPE, Ann. Tech. Conf. and Exh.

Solle, D., Hitzmann, B., Herwig, C., Remelhe, P.M., Ulonska,
S., Wuerth, L., Prata, A., and Steckenreiter, T. (2016). 
Between the poles of data-driven and mechanistic
modeling for process operation. Chemie Ingenieur Technik.

White, F. (2003). Fluid Mechanics, 5th edition. McGraw-
Hill, New York, USA.

Xu, L., Zhou, W., Li, X., and Tang, S. (2011). Wet
gas metering using a revised venturi meter and softcomputing
approximation techniques. IEEE transactions on instrumentation and measurement, 60, 947-956.

Zendehboudi, S., Rezaei, N., and Lohi, A. (2018). Application
of hybrid models in chemical, petroleum, and
energy systems: A systematic review. Applied Energy,
228, 2539-2566.