10-100xfaster screening
AI-enriched Simulation: Understanding more about data with less computing.
*By reducing the number of simulations needed, AI-enriched simulation can speed up screening by factors of 10-100x.
AI-enriched Simulation accelerates the discovery process by using AI to identify the most promising simulations to run on a massive data set. Just as importantly, it determines the computing infrastructure best suited for the task—whether that’s a basic calculator or even, a Quantum computer.
This technology also removes a critical research bottleneck by making complex, predictive simulations automatable and user-friendly for researchers without deep computational expertise. Across use cases (ranging from drug discovery to chip design) AI-enriched Simulation has reduced time to results by a factor of 10-100x.
How it works
AI Enriched Simulation helps researchers maximize efficiency and efficacy at multiple points in the discovery process.
There are two keys to accelerating a process: doing things right (efficiency) and doing the right thing (effectiveness). In the world of modelling and simulation, this is a complicated problem.
Simulation workflows are notoriously complex. There are a multitude of datapoints, methods, and systems to choose from. Choose wrong at any point, and your entire experiment could be flawed before it's begun, costing dollars and hours of computing.
AI-enriched Simulation helps researchers determine what to study, and how to go about doing it. It efficiently optimizes the candidate search process, so researchers can focus their work on a smaller, more promising pool of options. And, it determines the simulation method that will minimize time and computational load, while yielding effective results.
Over time, our AI acceleration engine learns how to automate and streamline the simulation workflows, so what once took researchers hours to repetitively program and execute, becomes a simpler and “lighter” process.
Figure A1.
01
Finding the right data set.
The more broadly you look, the more likely you’ll find what you’re looking for. AI enables us to sample a vaster set of options quickly and efficiently—giving researchers the flexibility to cover more ground in less time and at a lower cost.
Using Bayesian optimization, a mathematical strategy that leverages known data to make predictions around unknown data (thus decreasing processing time), our AI Acceleration engine efficiently determines the data-points with the highest probability of fulfilling the desired parameters.
02
Identifying the best method.
In addition to deciding which candidates to test, AI helps us find the best way to test them.
Our engine uses a process called multi-fidelity optimization to determine both what kind of test, and what type of a computer resource, is needed to measure the desired factors at the lowest cost.
Being able to automatically “pair” the right model and infrastructure gives researchers the flexibility to balance speed, cost, and fidelity before moving on to testing.
Figure A2.
03
Running the simulation.
With the candidates and method set, researchers are ready to run the simulation. OpenShift, IBM’s hybrid cloud engine, connects cloud systems and architectures from around the world to create a flexible, fully-functioning simulation platform.
Rather than intricately programming the simulation across different computers and databases over and over again, researchers can simply use the platform to deploy the tests whenever needed. This automation saves hours of programming.
04
Streamlining the process.
Our AI-enriched simulation platform uses a productivity-boosted process called “memoization.” Whenever a user runs a workflow, the central 'brain' breaks it down and checks if these steps have ever been run before in previous workflows.
If there are repeats, the engine recycles (automates) the workflow—reusing the old result and shaving off computational time, energy, and money. Even with an entirely new workflow, AI can identify similar functions in its memory bank and give researchers the option to swap them in and streamline the workflow.
A more efficient way to get to impactful results. AI-enriched Simulation lightens the most computation-heavy points
of the discovery process, saving precious time and resources, and helping researchers get to testing faster.
Dr. Michael Johnston Ph.D.
