Key Points

Introduction to WarpX

🔮 The particle-in-cell method is used to simulate the dynamics of charged particles and their self-consistent fields

🚀 WarpX is an open-source particle-in-cell code

✨ WarpX is used in a variety of scientific domains

🏎️ WarpX is a high-performance code and can leverage HPC

Introduction to ImpactX

  • 🧲 ImpactX models how particle beams evolve through accelerator components.
  • 🎯 The lattice, incoming bunch, and enabled effects define the physical model.
  • 💰 Particle count, collective-field calculations, and diagnostics determine the workload.

Install

🎯 WarpX is easy to install via Conda: conda -c conda-forge warpx

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

Set Up a Simulation from Scratch

The WarpX examples and the documentation gallery are the best starting points for a new simulation.

Setting up a simulation is an iterative process: run, visualize, understand what’s wrong, fix it, repeat.

When the simulation doesn’t show the expected physics, check the basics first: enough timesteps? resolved scales? adequate particles per cell? temperature not too high?

The documentation parameter reference is your constant companion for understanding and modifying input files.

A Two-stream Instability

💡 The two-stream instability converts the kinetic energy of counter-streaming beams into electrostatic wave energy.

🔬 The instability growth rate depends on the ratio of drift velocity to thermal velocity: \(v_d / v_{\mathrm{th}} \gg 1\) is needed for the instability to develop.

📊 Reduced diagnostics (FieldEnergy, FieldMaximum) let you track the instability growth in real time without post-processing full datasets.

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python.

A Weibel Instability

💡 The Weibel instability generates magnetic fields from a momentum anisotropy – unlike the two-stream instability, which generates electrostatic fields.

🔬 The instability produces current filaments with a characteristic size of order the plasma skin depth \(c/\omega_{pe}\).

📊 Reduced diagnostics (FieldEnergy, FieldMaximum) are an efficient way to monitor the instability growth without storing large field dumps.

⚡ Choosing parameters wisely (\(v_d \gg v_{\mathrm{th}}\), sufficient resolution and box size) is essential for observing the instability clearly.

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python.

A Magnetic Mirror

💡 The external B field is loaded from an openPMD file, while the protons are defined as test particles.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python or you can open .pmd files directly in Paraview.

A FODO Cell

🎯 Particle tracking follows individual particles, while envelope tracking follows the beam envelope - particle tracking is more detailed but computationally expensive.

🔬 A FODO cell is a periodic focusing structure with alternating focusing and defocusing quadrupole magnets.

⚡ WarpX allows you to turn space charge effects on/off and define arbitrary external fields.

🚀 WarpX is a full PIC code best suited for strong space charge effects and complex electromagnetic interactions. For cases with negligible space charge, ImpactX may be a more efficient choice.

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python.

A Laser-Driven Ion Accelerator

💡 TNSA accelerates ions via a strong electrostatic sheath field created by hot electrons escaping the rear surface of a laser-irradiated thin target.

🔬 Key parameters controlling the maximum ion energy are the laser intensity (\(a_0\)), the target thickness, and the target density.

⚙️ Charge-conserving current deposition (esirkepov) and proper boundary conditions (PML, absorbing) are essential for this type of simulation.

⚡ This is a computationally expensive simulation – be prepared to iterate on resolution and target density to find a balance between physical fidelity and computational cost.

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python.

A Laser Wakefield Accelerator

  • 🏄 A laser drives a plasma wake; its longitudinal field can accelerate electrons.
  • 🧩 The grid, particles, and time steps determine which features are resolved and how much computation is required.
  • 🔍 Density, field, and energy plots provide complementary evidence; an energy spectrum alone does not identify an accelerated bunch.
  • 🎛️ Save the baseline and vary one setting at a time to interpret changes.

A Beam Transport Line

  • 🧲 Magnets change particle trajectories; drifts allow those changes to develop into different beam sizes.
  • 🔍 Screen histograms and rms-size curves connect the bunch to the lattice.
  • 🧩 More macroparticles sample the same bunch more finely.
  • 📏 The model omits collective fields, radiation, and apertures; interpret its diagnostics within those limits.

A Beam-Beam Collision

💅 There are several details one needs to take care when setting up a beam-beam simulation

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python.

OSSFE 2025 - Using WarpX, a general purpose particle-in-cell code

🚀 WarpX is a open-source high-performance particle-in-cell code

🎯 WarpX is easy to install via Conda: conda -c conda-forge warpx

🔍 The documentation is the first place to look for answers, otherwise check out our issues and discussions and ask there.

📷 To analyze and visualize the simulation results in openPMD format, you can use the openPMD-viewer library for Python or you can open .pmd files directly in Paraview.

UCB 2026 -- Two-Stream Instability Live Tutorial

Setting up a simulation is an iterative process: start from an existing example, modify, run, visualize, and refine.

The WarpX documentation and the examples gallery are the best starting points.

When things don’t look right, check the basics: enough timesteps, adequate resolution, and appropriate temperature.

US-FCC 2026: Beam-Beam and Tracking Tutorial

  • WarpX resolves one collision in detail and writes both particle snapshots and collider-specific reduced diagnostics.
  • The laptop input prioritizes runtime and teaching value over numerical convergence.
  • Matching particle IDs between diagnostics makes the normalized kick slope a direct check of the linear beam-beam model.
  • Keeping different QED products in separate species makes their spectra and weighted yields easier to inspect.
  • The luminosity, Bhabha cross-section, lifetime, and pair-distribution checks connect the openPMD and reduced diagnostics to accelerator observables.
  • Using one MAD-X sequence and one matched covariance model makes the Tutorial 2 beta-function comparison sensitive to differences in lattice translation and linear transport.
  • The Tutorial 3 adapter converts normalized momentum and geometric slope separately; this prevents beamstrahlung energy loss from contaminating the transverse drift.
  • A beamstrahlung-induced \(\sigma_z\) oscillation is not automatically a coupling error: a local increase in energy spread creates a longitudinal mismatch that the arc map rotates into bunch length.
  • Per-collision handoff logs distinguish an instantaneous coordinate jump from subsequent synchrotron motion.

LLNL HPC Innovation Center 2026: WarpX/ImpactX Tutorial

  • 🧩 Physics and numerical representation determine the workload; hardware determines how that workload is executed.
  • 📝 Record the input, hardware, elapsed runtime, and output alongside the plots.
  • 💾 More particles, finer grids, and more frequent diagnostics have different scientific benefits and computational costs.
  • 🔍 Compare the same problem to measure a hardware speedup; compare physical observables to judge whether a numerical change matters scientifically.