I'm trying to create a particle simulation (solar system kind). Until now my particles have no spin so the collision is rather simple

    void collide(const Particle& b) {
        const Vector3d normal = math::normal(position(), b.position());
        const Vector3d relativeVelocity = velocity() - b.velocity();
        const double dot = math::dot(relativeVelocity, normal);
        const Vector3d work = normal * dot;

        _velocity = _velocity - work;
    }

Since I've read that particle spin plays a huge part in such simulations I'm trying to implement angular momentum but unfortunately this exceeds my math skills. I tried searching the internet for quite a while now for any source I understand but I'm at the brink of giving up.

How can I integrate particle spin into my code? I can work with any kind of (pseudo) code as long as the variables are somewhat clear. Particles are rigid spheres with mass = volume.

Edit: Dont get hang up on what the simulation is trying to achieve. The task can be simplified down to: Two rigid spheres collide in space. Calculate their motion and spin after the collision.

I am currently trying to figure out how to efficiently detect collisions in combination with a boid simulation in a 3D Minecraft clone written in java.

For single entities i already implemented a box collider implementation which checks against nearby voxels, but for hundreds to thousands of boids this seems like a huge overhead. As boids tend to stick together i believe there might be a better approach than checking every single boid in the flock if it collides.

I was thinking about precomputing a signed distance field for each voxel. This could then be used by the boids to sample if they need to steer away from any obstacles. (Resulting in more of a collision avoidance).

The issue i am trying to solve is how to implement perching for boids. I would like my boids to land on the ground from time to time. I am not sure if this is even possible using only an SDF.

A technical paper titled “Impact of Technology Scaling and Back-End-of-the-Line Technology Solutions on Magnetic Random-Access Memories” was published by researchers at Georgia Institute of Technology.

Abstract:

“While magnetic random-access memories (MRAMs) are promising because of their nonvolatility, relatively fast speeds, and high endurance, there are major challenges in adopting them for the advanced technology nodes. One of the major challenges in scaling MRAM devices is caused by the ever-increasing resistances of interconnects. In this article, we first study the impact of shrunk interconnect dimensions on MRAM performance at various technology nodes. Then, we investigate the impact of various potential back-end-of-the-line (BEOL) technology solutions at the 7 nm node. Based on interconnect resistance values from technology computer-aided design (TCAD) simulations and MRAM device characteristics from experimentally validated/calibrated physical models, we quantify the potential array-level performance of MRAM using SPICE simulations. We project that some potential BEOL technology solutions can reduce the write energy by up to 34.6% with spin-orbit torque (SOT) MRAM and 29.0% with spin-transfer torque (STT) MRAM. We also observe up to 21.4% reduction in the read energy of the SOT-MRAM arrays.”

Find the technical paper here. Published January 2024.

P. Kumar, D. E. Shim, S. Narla and A. Naeemi, “Impact of Technology Scaling and Back-End-of-the-Line Technology Solutions on Magnetic Random-Access Memories,” in IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 10, pp. 13-21, 2024, doi: 10.1109/JXCDC.2024.3357625.

Related Reading
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Why this 25-year-old technology may be the memory of choice for leading edge designs and in automotive applications.
ReRAM Seeks To Replace NOR
There is increased interest in ReRAM for embedded computing, especially in automotive applications, as more of its known issues are solved. Nevertheless, there is no one-size-fits-all NVM.

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