Jacobian matrix ($J_f$)

The Jacobian matrix contains all first partial derivatives of a coordinate transformation.

An approximation to a Jacobian matrix obtained using finite differences instead of analytic partial derivatives. For a vector-valued function $F$, the forward-difference entry is $J_{ij}\approx (F_i(x+h{e}_j)-F_i(x))/h$, where $e_j$ is a coordinate basis vector. The step $h$ balances truncation and rounding errors; the resulting matrix can be used in a Newton root-finding iteration.

To deduce $D{u}_t=I+tDF+o(t)$ from $u_t(x)=x+tF(x)+o(t)$, the remainder must be small in a topology controlling its spatial derivatives, such as $C^1$ locally. Small displacement alone is insufficient: $u_t(x,y,z)=(x+t^2\sin (x/t^2),y,z)$ differs uniformly from the identity by $o(t)$, but has Jacobian determinant $2$ at $x=0$ for nonzero $t$. A smooth flow map generated by a smooth vector field has the required derivative control.

The Jacobian determinant is the determinant of the square Jacobian matrix. Its absolute value is the local volume-scaling factor in the change of variables formula.

The Jacobian matrix and determinant describe respectively the linear derivative and its signed volume factor.

For a smooth map $u:{\mathbb{R}}^3\to {\mathbb{R}}^3$, set $V_i={ϵ}_{ijk}{ϵ}_{abc}({\partial }_j{u}_a)({\partial }_k{u}_b)u_c/6$. In Einstein summation convention, applying the product rule leaves only the term differentiating $u_c$: the terms with second derivatives cancel against antisymmetry. The remaining Levi-Civita symbol contraction is $\det Du$, so $\nabla \cdot V=J[u]$. Equivalently this vector field is one third of the cofactor matrix transposed, applied to $u$.