ENIGMA workshops and general meeting
6 February 2018 - 15 February 2018
Lausanne, Switzerland
The ENIGMA network is setting out to train a new generation of young researchers in the development of innovative sensors, field survey techniques and inverse modelling approaches. To further this aim the project is hosting workshops on ‘Predictive modelling and upscaling of flow and reactive transport in heterogeneous aquifers’ and another on 'Advanced inverse modeling and stochastic representations of heterogeneous porous and fractured media'. On the 9-10 the project will hold its third general meeting.
The programme will cover stochastic approaches of uncertainty analysis and model legitimacy, including field methods and involves industrial case studies on simulating flow and transport for waste storage problems (there will be a presentation by the industrial partner SKB), among other subjects.
ENIGMA (European training Network for In situ imaGing of dynaMic processes in heterogeneous subsurfAce environments) will place special emphasis on mixing and mixing-controlled reactions in heterogeneous media and will use field methods for the identification and characterisation of non-ideal behaviour. Hands on practice, using simple problems, will illustrate predictive modelling honouring extensive data sets and acknowledges uncertainty.
For more information, please see:
Event page(opens in new window)
The programme will cover stochastic approaches of uncertainty analysis and model legitimacy, including field methods and involves industrial case studies on simulating flow and transport for waste storage problems (there will be a presentation by the industrial partner SKB), among other subjects.
ENIGMA (European training Network for In situ imaGing of dynaMic processes in heterogeneous subsurfAce environments) will place special emphasis on mixing and mixing-controlled reactions in heterogeneous media and will use field methods for the identification and characterisation of non-ideal behaviour. Hands on practice, using simple problems, will illustrate predictive modelling honouring extensive data sets and acknowledges uncertainty.
For more information, please see:
Event page(opens in new window)