Piotr Trochim
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View article: Fast transport simulations with higher-fidelity surrogate models for ITER
Fast transport simulations with higher-fidelity surrogate models for ITER Open
A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The model consists of a set of neural networks trained on a bespoke quasilinear GENE dataset, with a saturation rule calibrated to dedicated non…
View article: Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters Open
Linear instability and quasilinear fluxes calculated with the GENE plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the QuaLiKiz transp…
View article: Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters Open
Linear instability and quasilinear fluxes calculated with the GENE plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the QuaLiKiz transp…
View article: Semi-analytical Industrial Cooling System Model for Reinforcement Learning
Semi-analytical Industrial Cooling System Model for Reinforcement Learning Open
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity an…
View article: Evaluating model-based planning and planner amortization for continuous control
Evaluating model-based planning and planner amortization for continuous control Open
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a…
View article: Learning Dynamics Models for Model Predictive Agents
Learning Dynamics Models for Model Predictive Agents Open
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner}. Much of the recent research along these lines presents a…
View article: Using Unity to Help Solve Intelligence
Using Unity to Help Solve Intelligence Open
In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing platforms for constructing such environments are typically con…
View article: Augmenting learning using symmetry in a biologically-inspired domain
Augmenting learning using symmetry in a biologically-inspired domain Open
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such…