Abstract: Methods and computer systems for transforming a model of a first scientific computing language to a model (e.g., surrogate) of a second scientific computing language such that the surrogate is trained across a plurality of possible inputs is disclosed. Model inputs in a first scientific computing language are received. A surrogate is generated based on the received model input. The surrogate may be trained across a plurality of possible inputs by selecting an input function representation for the model input, selecting a parameter space, sampling the parameter space to generate a training set of time series for each parameter set, simulating a reservoir, computing projections from the simulated reservoir, and fitting an interpolating function between the projections to establish an approximate projection for unknown input functions and parameter values. The surrogate is then deployed, either trained or untrained depending on the embodiment.

Abstract: Systems and methods for design optimization using a multiple-data fitting interface are disclosed. Data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items are received. The received data objects are paired into a collection that includes dependencies between the data objects. A virtual population is generated based on the received data objects. The virtual population comprises multiple virtual patients, with each virtual patient comprising a combination of model parameters that describe data in the received data objects. A prediction for the virtual population is generated by simulating with each of the virtual patients. The generation of the virtual population includes a user-determined or default cost function and an algorithm for finding an optimal configuration for the virtual population with respect to said cost. An output is generated that includes a visualization of the prediction.

Abstract: Systems and methods of component-based modeling using trained surrogates are disclosed. A component architecture is made up of composable subsystem components. The composable subsystem components are reusable, such that the subsystem components are trained, and a library of trained model-reduced forms is created, which allows for large-scale models to be automatically accelerated for modeling. In this way, complex models are built by stitching together pre-designed, pre-shrunk components consisting of self-contained systems. A novel combination of using surrogates for modeling purposes and accelerated solving is provided to create an architecture that simulates complex physical processes that were previously infeasible to simulate in a commercially reasonable time.

Abstract: In one embodiment, a process may be performed in parallel on a parallel server by defining a data type that may be used to reference data stored on the parallel server and overloading a previously-defined operation, such that when the overloaded operation is called, a command is sent to the parallel server to manipulate the data stored on the parallel server. In some embodiments, the previously-defined operation that is overloaded may be an operation of an operating system. Further, in some embodiments, when the data stored on the parallel server is no longer needed, a command may be sent to the parallel server to reallocate the memory used to store the data.