minor wording changes

Author: radka-j

paper/paper.md

@@ -87,15 +87,15 @@ Computational simulations lie at the heart of modern science and engineering, bu
 
 # Statement of need
 
-Complex physical systems are often modelled using computer simulations. Depending on the complexity of the system, these simulations can be computationally expensive and time-consuming. This bottleneck can be resolved by approximating simulations with emulators, which can be orders of magnitudes faster [@kennedy_ohagan_2000]. Emulators are key to enabling any computationally expensive downstream tasks that require generating predictions for a large number of inputs. These tasks include sensitivity analysis to quantify the impact of each input parameter on the output as well as model calibration to identify input values most likely to have generated real-world observations.
+Physical systems are often modelled using computer simulations. Depending on the complexity of the system, these simulations can be computationally expensive and time-consuming. This bottleneck can be resolved by approximating simulations with emulators, which can be orders of magnitudes faster [@kennedy_ohagan_2000]. Emulators are key to enabling any computationally expensive downstream tasks that require generating predictions for a large number of inputs. These tasks include sensitivity analysis to quantify the impact of each input parameter on the output as well as model calibration to identify input values most likely to have generated real-world observations.
 
 Emulation requires significant expertise in machine learning as well as familiarity with a broad and evolving ecosystem of tools for model training and downstream tasks. This creates a barrier to entry for domain researchers whose focus is on the underlying scientific problem. AutoEmulate [@autoemulate] lowers the barrier to entry by automating the entire emulator construction process (training, evaluation, model selection, and hyperparameter tuning). This makes emulation accessible to non-specialists while also offering a reference set of cutting-edge emulators, from classical approaches (e.g. Gaussian Processes) to modern deep learning methods, enabling benchmarking for experienced users.
 
-AutoEmulate v1.0 introduces easy-to-use interfaces for common emulation tasks. By providing these tasks within a single package it enables users to construct sequential workflows. For instance, sensitivity analysis can be applied in order to narrow down the parameter space to the key variables. This allows the user to calibrate the much smaller reduced set to match the output of the model to real-world observations. AutoEmulate also supports direct integration of custom simulators and active learning, in which the tool adaptively selects informative simulations to run to improve emulator performance at minimal computational cost.
+AutoEmulate v1.0 introduces easy-to-use interfaces for common emulation tasks. By providing these tasks within a single package it enables users to construct sequential workflows. For instance, sensitivity analysis can be applied in order to narrow down the parameter space to key variables. This allows the user to calibrate the much smaller reduced set to match the output of the model to real-world observations. AutoEmulate also supports direct integration of custom simulators and active learning, in which the tool adaptively selects informative simulations to run to improve emulator performance at minimal computational cost.
 
 AutoEmulate was originally built on scikit-learn, which is well suited for traditional machine learning but less flexible for complex workflows. Version 1.0 introduces a PyTorch [@pytorch] backend that provides GPU acceleration for faster training and inference and automatic differentiation via PyTorch’s autograd system. It also makes AutoEmulate easy to integrate with other PyTorch-based tools. For example, the PyTorch refactor enables fast Bayesian model calibration using gradient-based inference methods such as Hamiltonian Monte Carlo exposed through Pyro [@pyro].
 
-Lastly, AutoEmulate v1.0 expands the set of implemented emulators, focusing particularly on predictive uncertainty quantification through adding ensemble methods, and improves support for high-dimensional data through dimensionality reduction techniques such as principal component analysis (PCA) and variational autoencoders (VAEs). The software's modular design centred around a set of base classes for each component means that the toolkit can be easily extended by users with new emulators and transformations.
+Lastly, AutoEmulate v1.0 expands the set of implemented emulators, with a particular emphasis on predictive uncertainty quantification through ensemble methods. It also improves support for high-dimensional data through dimensionality reduction techniques such as principal component analysis (PCA) and variational autoencoders (VAEs). The software's modular design centred around a set of base classes for each component means that the toolkit can be easily extended by users with new emulators and transformations.
 
 AutoEmulate fills a gap in the current landscape of emulation tools as it is both accessible to newcomers while offering flexibility and advanced features for experienced users. It also uniquely combines emulator training with support for a wide range of downstream tasks such as sensitivity analysis, model calibration and active learning.