What could be more important that using AI to learn how to do science more effectively, to learn what doing science really means, to update good methods for modelling our universe and make great?

In my lab we have done work on a few focussed topics in this area:

- Combustion Modelling - We used Deep Learning to greatly improve the speed and scale possible for existing flamelet estimation models.

- Material Design - In this ongoing work with the National Research Council, we are investigating exciting ways to apply Reinforcement Learning to the problem of material design and digital chemistry.

## Our Papers on AI for Science

Balancing Information with Observation Costs in Deep Reinforcement Learning

*In Canadian Conference on Artificial Intelligence*
Canadian Artificial Intelligence Association (CAIAC),
Toronto, Ontario, Canada,
may,
2022.

The use of Reinforcement Learning (RL) in scientific applications, such
as materials design and automated chemistry, is increasing. A major
challenge, however, lies in fact that measuring the state of the system
is often costly and time consuming in scientific applications, whereas
policy learning with RL requires a measurement after each time step. In
this work, we make the measurement costs explicit in the form of a
costed reward and propose the active-measure with costs framework that
enables off-the-shelf deep RL algorithms to learn a policy for both
selecting actions and determining whether or not to measure the state of
the system at each time step. In this way, the agents learn to balance
the need for information with the cost of information. Our results show
that when trained under this regime, the Dueling DQN and PPO agents can
learn optimal action policies whilst making up to 50% fewer state
measurements, and recurrent neural networks can produce a greater than
50% reduction in measurements. We postulate the these reduction can
help to lower the barrier to applying RL to real-world scientific
applications.

Scientific Discovery and the Cost of Measurement â€“ Balancing Information and Cost in Reinforcement Learning

*In 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE)*
feb,
2022.

The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that measuring the state of the system is often costly and time consuming in scientific applications, whereas policy learning with RL requires a measurement after each time step. In this work, we make the measurement costs explicit in the form of a costed reward and propose a framework that enables off-the-shelf deep RL algorithms to learn a policy for both selecting actions and determining whether or not to measure the current state of the system at each time step. In this way, the agents learn to balance the need for information with the cost of information. Our results show that when trained under this regime, the Dueling DQN and PPO agents can learn optimal action policies whilst making up to 50% fewer state measurements, and recurrent neural networks can produce a greater than 50% reduction in measurements. We postulate the these reduction can help to lower the barrier to applying RL to real-world scientific applications.

Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning

*In Canadian Conference on Artificial Intelligence*
Springer,
2021.

Markov Decision Processes (MDP) with explicit measurement cost are a class of en- vironments in which the agent learns to maximize the costed return. Here, we define the costed return as the discounted sum of rewards minus the sum of the explicit cost of measuring the next state. The RL agent can freely explore the relationship between actions and rewards but is charged each time it measures the next state. Thus, an op- timal agent must learn a policy without making a large number of measurements. We propose the active measure RL framework (Amrl) as a solution to this novel class of problem, and contrast it with standard reinforcement learning under full observability and planning under partially observability. We demonstrate that Amrl-Q agents learn to shift from a reliance on costly measurements to exploiting a learned transition model in order to reduce the number of real-world measurements and achieve a higher costed return. Our results demonstrate the superiority of Amrl-Q over standard RL methods, Q-learning and Dyna-Q, and POMCP for planning under a POMDP in environments with explicit measurement costs.

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

*In Canadian Conference on Artificial Intelligence*
Springer,
Ottawa, Canada (virtual),
may,
2020.

Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks

Sushrut Bhalla,
Matthew Yao,
Jean-Pierre Hickey,
and *Mark Crowley*

*In European Conference on Machine Learning (ECML-19)*
Wurzburg, Germany,
2019.

The computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it intractable to compute scaleesolving simulations. For most engineering applications, the large scale separation between the flame (typically submillimeter scale) and the characteristic turbulent flow (typically centimeter or meter scale) allows us to evoke simplifying assumptionsâ€“such as done for the flamelet model to precompute all the chemical reactions and map them to a low order manifold. The resulting manifold is then tabulated and looked up at runtime. As the physical complexity of combustion simulations increases (including radiation, soot formation, pressure variations etc.) the dimensionality of the resulting manifold grows which impedes an efficient tabulation and look up. In this paper we present a novel approach to model the multidimensional combustion manifold. We approximate the combustion manifold using a neural network function approximator and use it to predict the temperature and composition of the reaction. We present a novel training procedure which is developed to generate a smooth output curve for temperature over the course of a reaction. We then evaluate our work against the current approach of tabulation with linear interpolation in combustion simulations. We also provide an ablation study of our training procedure in the context of overfitting in our model. The combustion dataset used for the modeling of combustion of H2 and O2 in this work is released alongside this paper.