Emulating the interstellar medium chemistry with neural operators

• URL: http://arxiv.org/abs/2402.12435v1
• Date: Mon, 19 Feb 2024 19:00:01 GMT
• Title: Emulating the interstellar medium chemistry with neural operators
• Authors: Lorenzo Branca and Andrea Pallottini
• Abstract summary: Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM) Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. Compared with the reference solutions obtained by $textttKROME$ for single zone models, the typical precision obtained is of order $10-2$, i.e
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks. We use $\texttt{KROME}$ to generate a training set spanning $-2\leq \log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$, $-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field $\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared with the reference solutions obtained by $\texttt{KROME}$ for single zone models, the typical precision obtained is of order $10^{-2}$, i.e. the $10 \times$ better with a training that is $40 \times$ less costly with respect to previous emulators which however considered only a fixed $\mathbf{F}$. The present model achieves a speed-up of a factor of $128 \times$ with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modeling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency.

Related papers

• Optimized Quantum Simulation Algorithms for Scalar Quantum Field Theories [0.3394351835510634]
  We provide practical simulation methods for scalar field theories on a quantum computer that yield improveds. We implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of $4times 106$ physical qubits and $1012$ $T$-gates.
  arXiv  Detail & Related papers  (2024-07-18T18:00:01Z)
• Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
  We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$. We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
  arXiv  Detail & Related papers  (2024-01-17T18:59:38Z)
• Learning stiff chemical kinetics using extended deep neural operators [0.0]
  We develop a neural operator-based surrogate model to solve stiff chemical kinetics. This work aims to develop a neural operator-based surrogate model to solve stiff chemical kinetics.
  arXiv  Detail & Related papers  (2023-02-23T18:57:08Z)
• Neural networks: solving the chemistry of the interstellar medium [0.0]
  Non-equilibrium chemistry is among the most difficult tasks to include in astrophysical simulations. PINN-powered simulations are a palatable way to solve complex chemical calculation in astrophysical and cosmological problems.
  arXiv  Detail & Related papers  (2022-11-28T19:00:01Z)
• Studying chirality imbalance with quantum algorithms [62.997667081978825]
  We employ the (1+1) dimensional Nambu-Jona-Lasinio (NJL) model to study the chiral phase structure and chirality charge density of strongly interacting matter. By performing the Quantum imaginary time evolution (QITE) algorithm, we simulate the (1+1) dimensional NJL model on the lattice at various temperature $T$ and chemical potentials $mu$, $mu_5$.
  arXiv  Detail & Related papers  (2022-10-06T17:12:33Z)
• Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning [59.71676469100807]
  This work sharpens the sample complexity of synchronous Q-learning to an order of $frac|mathcalS|| (1-gamma)4varepsilon2$ for any $0varepsilon 1$. Our finding unveils the effectiveness of vanilla Q-learning, which matches that of speedy Q-learning without requiring extra computation and storage.
  arXiv  Detail & Related papers  (2021-02-12T14:22:05Z)
• Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $\sigma-$phase as an example [1.8369974607582582]
  We show how machine learning can be used to predict several properties in solid-state chemistry. In particular, it can be used to predict the heat of formation of a given complex crystallographic phase.
  arXiv  Detail & Related papers  (2020-11-21T22:07:15Z)
• Even more efficient quantum computations of chemistry through tensor hypercontraction [0.6234350105794442]
  We describe quantum circuits with only $widetildecal O(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary orbitals. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis.
  arXiv  Detail & Related papers  (2020-11-06T18:03:29Z)
• Mapping the charge-dyon system into the position-dependent effective mass background via Pauli equation [77.34726150561087]
  This work aims to reproduce a quantum system composed of a charged spin - $1/2$ fermion interacting with a dyon with an opposite electrical charge.
  arXiv  Detail & Related papers  (2020-11-01T14:38:34Z)
• Beyond Lazy Training for Over-parameterized Tensor Decomposition [69.4699995828506]
  We show that gradient descent on over-parametrized objective could go beyond the lazy training regime and utilize certain low-rank structure in the data. Our results show that gradient descent on over-parametrized objective could go beyond the lazy training regime and utilize certain low-rank structure in the data.
  arXiv  Detail & Related papers  (2020-10-22T00:32:12Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.